CN101438335A - Assessing road traffic conditions using data from mobile data sources - Google Patents

Abstract

Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads, as well as in some situations data from one or more other sources (such as physical sensors near to or embedded in the roads). The assessment of road traffic conditions based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Description

Use is from the data estimation road traffic condition in mobile data source

Technical field

Following open text relates generally to the technology that a kind of data of obtaining from various data sources are estimated road traffic condition, for example by infer the information of relevant traffic on these roads based on the data sample that has reflected the actual travel on the road interested.

Background technology

Because road traffic is to continue to increase than bigger ground of road capacity speed, the traffic congestion of surge is to commercial and government operation and individual happiness generation ill effect.Therefore, carried out the traffic congestion that surge is resisted in various effort in every way, offered individuals and organizations such as the information by obtaining current traffic condition and with information.Can be (for example by variety of way, via radio-frequency (RF) broadcast, internet site, internet site has shown the map of geographic area, wherein current traffic congestion is by the coloud coding information representation on some main roads of this geographic area, and information can send to cellular mobile phone and other portable consumer device etc.) such current traffic condition information is offered interested parties.

A kind of source that obtains relevant current traffic condition information comprises that the observation that manually provides (for example, the helibus of the relevant magnitude of traffic flow and accident general information is provided, report of sending via mobile phone by the driver etc.), and in some more large-scale metropolitan areas, another kind of source is the traffic sensor network, it can measure the magnitude of traffic flow (for example, by being embedded in the sensor in the pavement of road) of different kinds of roads in the zone.Although the observation that manually provides can provide some values under condition of limited, such information only limits to a few regions usually at every turn and lacks the enough details that are enough to use usually.

In some cases, the traffic sensor network can provide the more detailed information of some road traffic conditions.But there are variety of issue in such information and the information that is provided by other similar source.For example, a lot of roads do not have path sensor (for example, do not have the geographic area of path sensor and/or be not large enough to have path sensor and as closing on the arterial highway of a network part), even the road with path sensor also may often can not offer precise data, and this has greatly weakened the data value that traffic sensor provided.Non-a kind of reason accurate and/or non-authentic data comprises that traffic sensor damages, thereby data can not be provided, or the interruption data are provided, or correct reading of data.Non-another kind of reason accurate and/or non-authentic data is included in the problem that one or more sensors carry out the temporary transient transmission of data, causes being interrupted transmitting, or postpones to transmit, or do not transmit data.In addition, a lot of sensors do not dispose or design (is for example reported relevant driver condition, whether their function is normal), even if reported that driver's status information is also possible incorrect (for example, report driver function normally but in fact really not so), so just be difficult to maybe can not be definite whether accurate by data that traffic sensor provided.In addition, the information of relevant traffic only can obtain with original and/or discrete form, thereby uses limited.

Hide, it is very helpful providing a kind of improved technology to obtain and estimating the information of relevant traffic and various relevant additional abilities are provided.

Description of drawings

Fig. 1 illustrates the block scheme that is used at least in part estimating based on the data of being obtained from vehicle and other mobile data source the data stream between the assembly of embodiment of system of road traffic condition.

Fig. 2 A-2E illustrates the example of estimating road traffic condition at least in part based on the data of obtaining from vehicle and other mobile data source.

Fig. 3 is the block scheme that illustrates the computing system that is suitable for carrying out described data sample management system (DataSample Manager System) embodiment.

Fig. 4 is the process flow diagram of the exemplary embodiment of data sample filtrator routine.

Fig. 5 is the process flow diagram of the exemplary embodiment of data sample exceptional value remover (Outlier Eliminator) routine.

Fig. 6 is the process flow diagram of the exemplary embodiment of data sample speed estimator routine.

Fig. 7 is the process flow diagram of the exemplary embodiment of data sample flow estimation device routine.

Fig. 8 is the process flow diagram that mobile data source information provides the exemplary embodiment of routine.

Fig. 9 A-9C illustrates the action example that obtains and mobile data source in the relevant road traffic condition is provided.

Figure 10 A-10B illustrates the example of the data sample that correction obtains from road traffic sensors.

Figure 11 is the process flow diagram of the exemplary embodiment of sensing data read error detector routine.

Figure 12 is the process flow diagram of the exemplary embodiment of sensing data read error corrector routine.

Figure 13 is the process flow diagram that sensing data reads the exemplary embodiment of gatherer routine.

Figure 14 is the process flow diagram of the exemplary embodiment of magnitude of traffic flow estimation device routine.

Embodiment

Based on the relevant data of the traffic of obtaining, the technology of estimation road traffic condition is described in every way, such as the vehicle that comes comfortable travels down and other mobile data source and/or from traffic sensor (for example, being embedded in the road or near physical sensors).In addition, at least some embodiment, the data sample that comes from the mobile data source can be used from the data in one or more other sources and replenish, such as the data that read by the physical sensors that obtains in road annex or road.Based on the data sample that is obtained (for example, from road traffic sensors, from each mobile data source or collect the data that data point reads) can comprise the various filtrations and/or the adjustment of data sample and reading to the estimation of road traffic condition, and the various deductions of interested traffic correlated characteristic and probability are determined.

As described, the road traffic condition information data of being obtained in certain embodiments by the mobile data source (for example can comprise, vehicle) a plurality of data samples that provide, from based on the data readings of the traffic sensor of road (for example being embedded in the loop sensor in the pavement of road) with from the data of other data source.Data can with such as the variety of way analysis of total vehicle total amount of estimating in the specific part of the interested road of average traffic speed and institute of estimation etc. so that determine interested traffic feature so that with in real time or the mode of (for example at reception bottom data sample and/or reading) that is bordering in real time carry out the definite of traffic.For example, the data of being obtained can be adjusted in every way to detect and/or to proofread and correct the mistake in data.If the road traffic condition information of being obtained is coarsely maybe can not represent interested actual traffic situation feature, then in each embodiment, can also filter in every way to remove data, comprise by near small part based on the non-interested data sample of road with according to the related data sample of other data sample and/or be considered as identical as the data sample of statistics exceptional value, in certain embodiments, filtration can also comprise execution related with data sample and specified link.The filtered data sample (for example can also comprise other reflection vehicle location or non-interested behavior, the vehicle that berths, vehicle spins etc. in parking lot or building) data sample and/or other can not represent the data sample that actual vehicle is travelled on interested road.Estimate that at least some embodiment the data of being obtained can comprise the traffic (for example, the magnitude of traffic flow and/or average traffic speed) that is identified for road network various piece in the specific geographical area at least in part based on the data sample that is obtained.Then can use the data of being estimated to carry out and relate to analysis, prediction, forecast, and/or other function of traffic relevant information is provided.In at least some embodiment, the data sample management system uses at least some described technology to prepare by the employed data of traffic data client, provide system such as the predicted traffic information that will produce a plurality of forecasts of traffic in a plurality of times in future, this will be in following detailed description.

In certain embodiments, the adjustment of fetched data sample can comprise the data sample that corrects mistakes, such as by detecting and/or proofread and correct mistake in the current data (for example, the data readings that receives from road traffic sensors) in every way.Particularly, such as the analysis based on the data sample that is provided by these data sources, the technology of " health " be used to estimate the particular source traffic sensor of road (for example based on) of describing is so that whether the specified data source is working properly and the precise information sample is provided reliably.For example, in certain embodiments, the former data readings that the current data reading that will be provided by given traffic sensor and this traffic sensor provide (for example, the historical average certificate) compares, to determine whether current traffic data reading is significantly different with in the past common data readings, for example this can be caused by the other problem in this traffic sensor non-normal working and/or the data, and/or can replace and reflect unusual current traffic condition.In each embodiment, can carry out this detection and analysis in every way to possible errors in particular source and/or the current traffic data reading, this will more go through following, comprise to small part based on such as the sorting technique that uses neural network, Bayes classifier, decision tree etc.

Detecting, can proofread and correct or revise this corrupt data sample (and data sample of losing) by this way such as behind corrupt data sample from the damaged data source of operate as normal not.For example in certain embodiments, can (for example revise one or more data sources by using one of relevant information or other to originate, traffic sensor) obliterated data and corrupt data, for example by from closing on or data sample is (for example the relevant traffic sensor of other operate as normal the time, by the data readings that is provided by adjacent traffic sensor is taken the mean), by relate to lose with the foresight information of corrupt data sample (for example, lead to the expected data reading that condition information is determined one or more data sources by foresight and/or the forecast sexual intercourse of using these data sources), via the historical information of one or more data sources (for example, by using historical average according to reading), via using relevant consistent deviation or other type of error that can compensate of leading to errors is adjusted with the correction data sample etc.Relate to revise lose with other details of corrupt data sample will be in following detailed description.

In addition, the technology of description also is used for various alternate manners estimation traffic related informations, such as the situation of the correction of the data sample that allows to carry out reliably particular source (for example, special traffic sensor) in current available data.For example, the existence of the unhealthy traffic sensor of a plurality of not operate as normal may cause not having enough data to come in these traffic sensors each estimated traffic flow information fully credibly.In this case, traffic related information may estimate with various alternate manners, comprises based on relevant traffic sensor group and/or relates to the out of Memory of road network structure.For example; as the following more detailed description ground of wanting; each interested road can come modeling or expression by using a plurality of road segment segment, and each road segment segment can have the traffic sensor of a plurality of associations and/or the data that obtain from one or more other data sources (for example, mobile data source).The words of if so, can estimate road traffic condition information at particular lane highway section (or other group of a plurality of relevant traffic sensors) in every way, for example be used to estimate the traffic related information of adjacent road section by use, the information of forecasting that is used for the particular lane highway section (for example, in the following time period limited, produce such as three hours etc., at least in part based on recent situation in current and the schedule time), to the forecast information in particular lane highway section (for example, in the following time period, produce, so that do not use some or all of the current and recent condition information that is used to predict) such as two weeks or longer time, the historical long-run average in particular lane highway section etc.By using such technology, even if when having only the current traffic condition data of a small amount of or neither one or a plurality of approaching sensor or other data source, also can provide traffic related information.Other details that relates to such traffic related information estimation will be in following detailed description.

As previously mentioned, the information of relevant road traffic condition can obtain in every way from the mobile data source in various embodiments.In at least some embodiment, the mobile data source comprises the vehicle on the road, its each comprise one or more computing systems that close the vehicle mobile data that provide.For example, every vehicle can comprise that GPS (" GPS ") equipment and/or other can determine geographic position, speed, direction and/or other sign or relate to the geolocation device of the data of vehicle ', and the one or more equipment on the vehicle (no matter whether being geolocation device or different communication facilities) can be with such data (for example sometimes, pass through Radio Link) (for example offer one or more systems that can use such data, the data sample management system will be in following more detailed description).For example, such vehicle can comprise the distributed network by the vehicle of each incoherent user's operation, fleet (for example, be used for express company (delivery company), taxi and bus company, carrier, government department or agency, the vehicle of car rental services etc.), being subordinate to provides relevant information (for example, the vehicle of the commercial network OnStar service), the vehicle group that is operated to obtain such traffic related information (for example, by the predetermined route that travels, or the dynamic direction that changes on road of travelling, to obtain the information of relevant interested road), the vehicle that is mounted with mobile telephone equipment (for example, as built-in device and/or have vehicle-mounted thing (vehicle occupant)) positional information (for example, based on the GPS ability of equipment and/or based on the geo-location ability that is provided by the mobile network) etc. can be provided.

In at least some embodiment, the mobile data source can comprise or based on other mobile device of the user of computing equipment and travels down, be the driver and/or the passenger of vehicle on the road such as the user.Such subscriber equipment can comprise equipment with GPS ability (for example, mobile phone and other handheld device), or position and/or mobile message alternatively also can otherwise produce in other embodiments.For example, equipment in vehicle and/or subscriber equipment can (for example communicate with the external system of energy detection and tracking relevant devices information, the equipment that passes through separately by a plurality of emittor/receivers in the network of system operation), thereby make the position of equipment and/or mobile message be determined with variety of way with various level of detail, perhaps such external system can also the relevant vehicle of detection and tracking and/or user's information and with equipment mutual (for example, can not observe and discern the camera system of driving board and/or user's face).For example, such external system can comprise mobile phone tower and network, other wireless network (for example, Wi-Fi Hotspot), use the various communication technologys the vehicle transducer detecting device (for example, RFID, or " radio frequency identification "), other detecting device of vehicle and/or user (for example, uses infrared ray, sonar, radar or Laser Distance Measuring Equipment are to determine the position and/or the speed of vehicle) etc.

Can use the road traffic condition information that obtains from the mobile data source in every way, no matter still be separately and use together from other road traffic condition information of one or more other sources (for example, from road traffic sensors).In certain embodiments, use such road traffic condition information that obtains from the mobile data source, provide info class to be similar to data from path sensor, but for do not have the operation path sensor road (for example, for the road that lacks sensor, such as for the geographic area that does not have networks of road sensors and/or not even as big as the arterial highway of sensor is arranged, for the path sensor that damages etc.), with the Copy Info of verification from path sensor or other source reception, thereby identification provides the path sensor (for example, because interim or current problem) of non-precise information etc.And road traffic condition can be measured or represents no matter be based on the data sample from mobile data source and/or traffic sensor data readings in one or more modes, for example aspect absolute in (for example, average velocity; The volume of traffic in the indicated time period; The average holding time of other position on one or more traffic sensors or the road is for example to represent that vehicle passes through or the average percentage of activated sensors time; The calculating grade of one or more congestion in road is for example measured based on one or more other traffics; Or the like) and/or aspect relatively (for example, the difference of expression and normal conditions or maximum case).

In certain embodiments, some road traffic condition information can be provided by the form of the data sample that provided by various data sources, and for example related with vehicle data source is with the travelling characteristic of reporting vehicle.Each data sample can comprise the quantity of information of variation.For example, the data sample that is provided by the mobile data source can comprise one or more come source identifier, speed identifier, orientation or direction indication, position indication, timestamp and status identifiers.Coming source identifier can be numeral or the string of sign as the vehicle (or people and miscellaneous equipment) of data source.In certain embodiments, the mobile data source identifier can be permanent with the mobile data source or be temporary transient related (for example, for life-span in mobile data source; For one hour; For the session of current use, for example so that unlocking vehicle or data-source device are just distributed a new identifier each time).In at least some embodiment, come source identifier related with the mobile data source, so that the secret relation from the data in mobile data source of relating to minimizes (no matter be forever or temporary transient related), for example by to stop the mode of discerning the mobile data source related based on identifier to be created and/or the operate source identifier with this mobile data source and identifier.The speed indication can reflect the instant or average velocity (for example, mph.) in the mobile data source of expression in every way.The orientation can reflect the direction of travelling, and is angle or other tolerance orientation or the radian of compass (for example, based on) with " degree " expression.Position indication can reflect the physical location (for example lat/lon to or Universal Transverse Mercator coordinate) of expression in every way.Timestamp can be indicated the time of mobile data source record sample preset time, for example with local zone time or UTC (" Universal Coordinated Time ") time.Status identifier can represent the mobile data source state (for example, vehicle move, stop, engine running and stops etc.) and/or at least some states (for example, electric weight is low, signal intensity is weak etc.) of sensing, record and/or transmitter.

In certain embodiments, the road network in given geographic area can come modeling or expression by using a plurality of road segment segment.Each road segment segment can be used to represent the part of road (or a plurality of road), for example by given physics road (for example is divided into a plurality of road segment segment, each road segment segment has particular length, such as one mile long road, or select to reflect that the road part of similar traffic feature is as road segment segment), a plurality of road segment segment like this can be the continuous parts of road, or alternatively in certain embodiments, and they can overlapping or any road segment segment all have the part of phase mutual interference.In addition, road segment segment can be represented the one or more traveling lanes on the given physics road.Therefore, on each of both direction, all have the specific multilane of one or more traveling lanes can be with the two road section is related at least, wherein at least one road segment segment and the direction travel related, and at least another with other direction on travel related.In addition, in some cases, a plurality of tracks of the single road that travels on single direction can be represented by a plurality of roadway segment, if for example the track has different travel conditions features.For example, given freeway facility can have quick or high occupancy (" HOV ") track, it can be represented with as quick or HOV track by the far different mode in routine (for example, the non-HOV) track of travelling on the equidirectional with expression.Roadway segment can also be connected to other adjacent road segment segment or the road segment segment adjacent with other related, thereby form the road segment segment network.

Fig. 1 illustrates the process flow diagram that is used at least in part based on the data stream between the assembly of the embodiment of the system of the data estimation road traffic condition that obtains from vehicle and other mobile data source.Shown data flowchart is intended to be reflected in data source, i.e. the logical expressions of the data stream between the assembly of the embodiment of data sample management system, and the traffic data client.That is to say, actual data stream may take place via various mechanism, comprise direct stream (for example, by realize or such as the network service of message) and/or via one or more Database Systems or other indirect stream such as the storage system of file system by parameter.Shown data sample management system 100 comprises data sample exceptional value removal assembly 106, data sample velocity estimation assembly 107, data sample stream estimation assembly 108 and optional sensor collection assembly 110.

In an illustrated embodiment, the assembly 104-108 of data sample management system 100 and 110 obtains data sample from various data sources, and this comprises data source 101, road traffic sensors 103 and other data source 102 based on vehicle.Can be included in a plurality of vehicles of one or more travels down based on the data source 101 of vehicle, its each can comprise one or more computing systems and/or can provide the miscellaneous equipment that closes the vehicle running data.As the other more detailed description ground wanted, every vehicle can comprise GPS and/or can define the geolocation device of closing position, speed and/or other data that vehicle travels.Such data can (for example be passed through wireless data link by the assembly of described data sample management system, satellite uplink and/or mobile telephone network) or alternate manner is (for example, after vehicle arrives certain physical location, for example after its base is got back to by fleet, carry out physics wired/cable connects) obtain.Road traffic sensors 102 can comprise and be installed in each street, highway or other road, go up or near a plurality of sensors, for example is embedded in loop sensor energy measurement time per unit in the road surface by the vehicle fleet size on this sensor, car speed and/or relate to other data of the magnitude of traffic flow.Data can be similarly from road traffic sensors 102 via obtaining based on wired or wireless data link.Other data source 103 can comprise the data source of various other types, comprise Map Services and/or database that relevant road network information is provided, for example link between road and the traffic control information (for example, the existence of traffic control signal and/or position and/or speed limit zone) that relates to this road.

Though the data source 101-103 in this example directly offers each assembly 104-108 and 110 of data sample management system 100 with data sample, data sample also can be handled earlier before being provided for these assemblies in other embodiments.Such processing can comprise based on the identity in time, position, geographic area and/or individual data source (for example, vehicle, traffic sensor etc.) tissue and/or collect data sample in logical collection.In addition, such processing can comprise that merging or data splitting sample are to more senior logical data sample or other value.For example, the data sample that the road traffic sensors of colocated obtains from a plurality of geography can be integrated with single logical data sample by average or other collection mode.In addition, such processing can comprise derives based on one or more data samples that obtain or the element of generated data sample or data sample.For example, in certain embodiments, at least some can provide the data sample that only comprises coming source identifier and geographic position based on each of the data source of vehicle, if so is so with specified time interval or section and a plurality of different data sample group that periodically provides just can be related with another and as particular vehicle was provided At All Other Times.Can also further handle such data sample group and determine other relevant information of travelling, for example the orientation of each data sample (for example, by calculating the angle between the position of the position of data sample and previous and/or subsequent data sample) and/or the speed of each data sample is (for example, by calculating the distance between the position of the position of data sample and previous and/or subsequent data sample, and will be apart from divided by the corresponding time).

In an illustrated embodiment, data sample filter assemblies 104 obtains data sample from data source 101 and other data source 102 based on vehicle, and before they being offered data sample exceptional value removal assembly 106 and offering data sample stream estimation assembly 108 alternatively the data sample that is obtained is filtered.As will more going through ground elsewhere, such filtration can comprise: with data sample with related corresponding to the road segment segment of road in the geographic area, and/or identification not corresponding to interested road segment segment or reflect the data sample of uninterested vehicle location or behavior.Can comprise data sample is related with road segment segment: use the reported position of each data sample and/or orientation to determine that whether this position and orientation are corresponding to previously defined road segment segment.Identification can not comprise corresponding to the data sample of the interested road segment segment of institute: remove or discern such data sample so as not to their modelings, consider or by other assembly processing of data sample management system 100, such data sample of removing can comprise that those corresponding to the road of the road class of uninterested specific function (for example, residential street) data sample, those data samples (for example, ramp and collector/distribution lane/tell highway road) etc. corresponding to the part of uninterested road or zone.Whether the recognition data sample reflects that uninterested vehicle location or behavior can comprise: discern and be in idle condition (for example, engine is leaving and stopping), drive the corresponding data sample of vehicle of (for example, spinning with low-down speed) etc. in the garage parking.In addition, in certain embodiments, filtration can be included as that to present or further analyze and discern road segment segment be that (or not being) is interested.For example, such filtration at special time period (for example can comprise analysis, hour, day, week) changeability of the interior magnitude of traffic flow and/or the degree of blocking up of each bar road segment segment, have (intra-time period) changeability in the low time period and/or to hang down some or all road segment segment of blocking up (for example, unavailable or their functional category of roads is represented littler or the road segment segment of travel still less for the sensing data reading) so that from further analysis, get rid of as uninterested road and road segment segment.

The sensing data adjuster 105 auxiliary data samples that correct mistakes are for example by detecting and proofread and correct from the mistake of the reading of road traffic sensors 103 acquisitions.In certain embodiments, adjusting component detection by sensing data is that insecure data sample is not forwarded to other assembly and uses and (or provide the non-reliable expression of particular data sample, so that other assembly can be handled these data samples), for example, be not forwarded to data sample exceptional value remover 106.If so, data sample exceptional value are removed assembly can determine whether that then enough authentic data samples can use, if not, then initiate the correction behavior.Alternatively, in some embodiment and environment, sensing data is adjusted assembly can also carry out some corrections to the data sample, will more go through ground as following, data after then will proofreading and correct offer sensor collection assembly 110 (also offer other assembly alternatively, for example the data sample exceptional value is removed assembly and/or data sample stream estimation assembly).Detect the misdata sample and can use various technology, comprise statistical measurement, will by the distribution of the current data sample of given road traffic sensors report with the corresponding time period (for example, identical week fate with one day in the identical time) in distribute by the history of the data sample of this road traffic sensors report and to compare.Difference actual and historical distribution range can be calculated by statistical measures, Kullback-Leibler divergence for example, and it provides the convex measuring of the similarity between two probability distribution, and/or the statistical information entropy.In addition, some path sensors can be reported the indication of sensor health, can also use such indication to detect the mistake of the data sample that is obtained.If in the data sample that is obtained, detect mistake, then can revise the data sample of makeing mistakes in every way, comprise that the mean value that is used to from the data sample on adjacent (for example, the next door) of determining error-free adjacent/next door path sensor replaces such data sample.In addition, can replace such as the value of before or simultaneously having predicted and/or having predicted that provides by predictive traffic information systems, revise the data sample of makeing mistakes by using.Other details that relating to predicted traffic information provides will provide in addition.

The data sample exceptional value is removed assembly 106 and is obtained the filtered data samples and/or adjust assembly 105 from sensing data to obtain to adjust or revised data samples from data sample filter assemblies 104, then identification and considering remove those do not represent the data sample that travels of the actual vehicle on interested road and the road segment segment.In an illustrated embodiment, for each interested road segment segment, block analysis is write down in special time period and the data sample group (for example, by data sample filter assemblies 104) related with road segment segment, if remove, which should remove to determine.Can carry out in every way and so non-representative data sample be determined that comprise based on following technology: with respect to other data sample in the data sample group, detecting data sample is the statistics exceptional value.Other details that relates to the removal of data sample exceptional value will provide in addition.

Data sample velocity estimation assembly 107 is removed assembly 106 from the data sample exceptional value and is obtained data samples, so as in the embodiment shown the data sample of Huo Deing be illustrated in the actual vehicle on interested road and the road segment segment travel.Data sample velocity estimation assembly 107 is then analyzed the data that obtained, with based on this road segment segment (for example, by data sample filter assemblies 104, or the reading that comes by sensor) the data sample group related with the time period from road segment segment part, estimate at least one in the interested time period one or more speed of interested road segment segment.In certain embodiments, the speed of being estimated can comprise that this organizes the speed average of a plurality of data samples, also can be by one or more attribute weights of data sample

(for example, the age (age) is so that give the newer bigger weighting of data sample; And/or the source of data sample or type, so that come to the bigger weighting in the source with higher expected reliability or availability from the mobile data source or from the weighting that path sensor changes data sample).The more details that relate to the velocity estimation that carries out from data sample will provide elsewhere.

Data sample stream estimation assembly 108 is interested road segment segment estimation telecommunication flow information at least one interested time period of institute, with the estimation volume of traffic (for example, be expressed as the vehicle total amount or the average that in such as per minute or special time amount hourly, arrive or pass through road segment segment), the estimation traffic density (for example, be expressed as such as every mile or kilometer etc. the vehicle average or the total amount of per unit distance) and estimation traffic occupancy (for example, be expressed as take specified point or regional average or total time quantum) etc. at for example per minute or the special time amount vehicle that per hour waits.In an illustrated embodiment, to the estimation of telecommunication flow information at least in part based on removing the information that relates to traffic speed that assembly 106 provides by data sample velocity estimation assembly 107 and data sample exceptional value, alternatively can be based on adjust the traffic data sample information that assembly 105 and data sample filter assemblies 104 provide by sensing data.Other details that relates to the estimation of traffic sample flow will provide elsewhere.

If exist, then such as adjust at sensing data assembly removed any insecure data sample and/or revised any lose and/or non-authentic data sample after, sensor data collection assembly 110 is collected by sensing data and is adjusted sensor-based traffic related information that assembly 105 provides.Alternatively, in other embodiments, the sensor data collection assembly can alternatively be carried out this losing and/or the removal and/or the correction of corrupt data sample.In some cases, sensor data collection assembly 110 can provide telecommunication flow information for each of these road segment segment by collecting information that (for example, average) provided by a plurality of independent traffic sensor related with each road segment segment.Similarly, if exist, sensor data collection assembly 110 can provide information, the estimation traffic that provides with the assembly that replenishes by for example data sample velocity estimation assembly 107 and/or data sample stream estimation assembly 108 etc., or can be reliable from the data sample in mobile data source or do not have the authentic data sample of q.s to allow other assembly such as data sample velocity estimation assembly 107 and data sample stream estimation assembly 108 etc. to provide under the situation of accurate estimation road traffic condition information alternatively to use.

The road traffic condition information that the estimation that provided by data sample velocity estimation assembly 107 and/or data sample stream estimation assembly 108 is provided one or more in an illustrated embodiment traffic data clients 109 (for example, speed and/or flow data), and can use such data in every way.For example, traffic data client 109 can comprise other assembly and/or by the traffic information system of the operator of data sample management system 100 operation, for example the foresight transport information provides system, uses traffic related information to be created in the traffic related information of the future transportation situation forecast of a plurality of following times; And/or in real time the transport information of (or being bordering in real time) presents system and obtains or provide system, and the traffic related information of (or being bordering on real-time) is provided in real time to terminal user and/or third party's client.In addition, traffic data client 109 can comprise that the computing system of being operated by the third party is to provide transport information to its client.In addition, in some environment (for example, when not carrying out accurate estimation for data sample velocity estimation assembly and/or data sample stream estimation assembly obtains enough data, and/or when from not obtaining under the data conditions based on vehicle or other data source) the road traffic condition information that provided by sensor data collection assembly 110 is provided these one or more traffic data clients 109 alternatively, can substitute data, or outside this, additionally obtain from data sample velocity estimation assembly and/or data sample stream estimation assembly.

For illustrated purpose, some embodiment wherein estimate the road traffic condition of particular type in a particular manner in following description, and use such estimation transport information in various specific modes.But, should be understood that, can be otherwise and use in other embodiments that the input data of other type produce such traffic estimation, described technology can used in other situation very widely, and the exemplary details that provided is provided in the present invention.

Fig. 2 A-2E illustrates the example based on the data estimation road traffic condition that obtains from vehicle and other mobile data source, as performed by described data sample management system.Particularly, Fig. 2 A illustrates the example that data sample filters, and is used to have several roads 201,202,203 and 204 and have an example region 200 of indication legend indication 309 in a northerly direction.In this example, road 202, such as the limited road (limited access road) that enters of the highway or the highway that crosses, be divided into the west to east orientation on the respectively different track group 202a and the 202b of driving vehicle.Track group 202a comprises HOV track 202a2 and a plurality of other conventional track 202a1, and track group 202b comprises HOV track 202b2 and a plurality of other conventional track 202b1 similarly.Road 201 is to walk road 202 (for example, via overline bridge or bridge), and road 204 is onramps, and its northern runway 201b with road 201 is connected to the eastbound carriageway group 202b of road 202.Road 203 is local frontage roads of adjacent road 202.

Can be illustrated in the road shown in Fig. 2 A in every way, to be used for described data sample management system.For example, one or more road segment segment can be related with each physics road, and are for example that the north row is related with northern runway 201a and southern runway 202b respectively with highway section, southern trade.Similarly, at least one head west road segment segment and at least one eastbound road segment segment are can be respectively related with head west track group 202a and the eastbound carriageway group 202b of road 202.For example, the part in the eastbound carriageway group 202b east of road 201 can be and the part in the group 202b west, track that heads west of road 201 road segment segment independently mutually, for example (for example change between road segment segment based on general road traffic condition or through being everlasting, because vehicle significantly flow into the track group 202b of road 201 from onramp 204 usually, so in general causes in bigger blocking up to the track group 202b of road 201 east orientations).In addition, one or more tracks group can be decomposed in a plurality of road segment segment, if for example different tracks (for example generally or often has different road traffic condition features, based on giving certain portions as first road segment segment track group 202b in these tracks of enjoying similar traffic feature corresponding to track 202b1, and will owing to its have different traffic features thereby as corresponding to the second lane section of HOV track 202b2)-in other this situation, have only single road segment segment can be used for such track group, but this track group of estimation road traffic condition the time some data samples (for example, corresponding to the 202b2 in HOV track those) can from use, get rid of (for example removing assembly) by data sample filter assemblies and/or data sample exceptional value.Alternatively, some embodiment can be expressed as the single road section with a plurality of tracks of a plurality of given roads, even if this track is a up train in the opposite direction, when for example road traffic condition is similar usually on both direction---for example, frontage road 205a can have two opposite driving lanes, but can be represented by a road segment segment.Road segment segment can otherwise come to determine at least in part at least some embodiment, for example related with geography information (for example, physical dimension and/or orientation and/or traffic relevant information (for example, speed limit).

A plurality of data sample 205a-k that Fig. 2 A has also described at specified time interval or a plurality of mobile datas source in zone 200 of travelling during the section (for example, 1 minute, 5 minutes, 10 minutes, 15 minutes etc.) At All Other Times (for example, vehicle, not shown) reported.By one of a plurality of mobile datas source report the time, each of data sample 205a-k all is illustrated as arrow, the orientation of its expression data sample.Data sample 205a-k is superimposed upon on the zone 200 by this way so that reflect that position that each data sample reports (for example, represent with dimension and precision unit, such as based on the GPS reading), its can be different when the record data sample with the physical location of vehicle (for example, because out of true or wrong reading, or because the intrinsic variable precision of employed position sensing mechanism).For example, data sample 205g has shown the slightly position in north of road 202b, it can reflect the vehicle that being drawn to 202b2 north side, track (for example, because mechanical fault), or it can be reflected in the non-exact position of the vehicle of actual travel on the eastbound direction in track 202b2 or other track.In addition, single mobile data source can be than shown in the source of data sample more data sample, if for example sample 205i and sample 205h are by (for example being reported along single portion vehicle that road 202 east orientations travel in the time period, by comprising the single transmission of a plurality of data samples that are used for a plurality of previous time points, so that per 5 minutes or per 15 minutes report data samples).About storing and providing the more details of a plurality of fetched data samples will be included in the following content.

Described in certain embodiments data sample management system can be filtered the data sample that is obtained, so as with data sample be mapped to predetermined road segment segment and/or identification not corresponding to the data sample of interested road segment segment.In certain embodiments, if reported position with the preset distance in corresponding road of road segment segment and/or track (for example, 5 meters) in, and its orientation with the predetermined angular (for example plus or minus 15 degree) in the orientation in corresponding road of this road segment segment and/or track in, then data sample is related with road segment segment.Though the association to the data sample of road segment segment can be used for carrying out before the data sample management system at data sample in other embodiments, road segment segment in the illustrated embodiment and enough location-based information are (for example, the orientation of road segment segment, the physical extent of road segment segment etc.) association is to make such determining.

As directed example, data sample 205a can be with related corresponding to the road segment segment of road 203, because its reported position drops in the scope of road 203 and at least one orientation identical (or being bordering on identical) of its orientation and related road 203.In certain embodiments, when using the single road section to be illustrated in a plurality of track of travelling on the opposite direction, can with two aspect ratios of the orientation of data sample and road segment segment than whether can be related with this road segment segment with determining data sample.For example, data sample 205k has roughly opposite with data sample 205a orientation, if but use road segment segment to represent two opposite carriageway of road 203, then it also can be with related corresponding to the road segment segment of road 203.

Yet, because road 203 is approaching with track group 202a, also possible is, because the orientation of data sample 205k is identical with the orientation of track group 202a, then data sample 205k is reflected in the vehicle that travels on the group 202a of track, if the blank space of the reported position of the data sample 205k vehicle location mistake of travelling in one or more tracks of track group 202a for example.In certain embodiments, a plurality of possible road segment segment situation of being used for a data sample can remove based on the out of Memory related with this data sample.For example, in this case, the analysis of the report speed of data sample 205k can help this removal, if for example group 202a in track is corresponding to the highway of 65mph speed limit, road 203 local frontage road for having the 30mph speed limit, and the speed reported of data sample is 75mph (cause with highway track related than big with the related possibility of local frontage road).In general, if the report speed of data sample 205k is compared the observation of track group 202a or observation or the transmission speed that transmission speed more is similar to road 203, then such information can be used for partly determining data sample and road 203 related rather than track group 202a.Alternatively, if the report speed of data sample 205k more is similar to observation or the transmission speed of track group 202a, then its just related with track group 202a rather than road 203 than the speed of observation or the road 203 that sends.Also can be used as a part (for example, position like the info class of other type in this removal; The orientation; State; Other relates to the information of data sample, for example other most recent data sample that comes from identical mobile data source etc.), for example reflect the matching degree of data sample information type and candidate roads section as the part of weighted analysis.

For example, for data sample 205b is related with the road segment segment that is fit to, the position that it is reported appears at track 201b and the overlapping part of track group 202a, and it closes on track 201a and other road.But the orientation that data sample is reported (roughly north row) is more approaching with the orientation of track 201b (north row) than the orientation of other candidate's track/road, thus in this example it probably with related corresponding to the road segment segment of track 201b.Similarly, data sample 205c comprises can mate a plurality of road/tracks (track 201a for example, 201b and track group 202a) reported position, but its orientation (roughly heading west) can be used to select be used for the road segment segment of track group 202a as being used for the only road segment segment of this data sample.

Still this example, data sample 205d can be not related with any road segment segment, because its orientation (roughly eastbound) and the reverse direction that is in corresponding to the track group 202a (heading west) of the reported position of this data sample.If there is not other suitable candidate roads section, the position that itself and data sample 205d are reported is enough near (for example, in predetermined distance), if it is too far away for example to have a track group 202b in similar orientation, then during filtering, get rid of this data sample from the follow-up use of the analysis of this data sample.

Data sample 205e can be related with the road segment segment such as corresponding to the road segment segment of HOV track 202a2 corresponding to track group 202a, this be because its reported position and orientation corresponding to the position and the orientation in this track, if for example being used for the location-based technology of the position of this data sample has enough resolution and (for example distinguishes the track, different GPS, infrared ray, sonar or radar ranging equipment).Data sample can also be based on the factor except position-based information and is related with the specific track of multiple-lane road, if for example the track has different traffic features.For example; in certain embodiments; can use the report speed of data sample to come the expection of the speed (or the magnitude of traffic flow other measure) by the data sample that is used for each such candidate track is observed (for example to distribute; usually or Gaussian distribution) modeling, and data sample conformed to specific track or mate.For example, because the speed reported of this data sample more approaches observation, deduction or the historical average speeds of the vehicle that travels on the 202a2 of HOV track than observation, deduction or historical average speeds at the vehicle that travels on the conventional track 202a1, therefore data sample 205e can be with related corresponding to the road segment segment of HOV track 202a2, for example by determine the analysis of observation or deduction speed (for example, using the data readings that provides by one or more road traffic sensors) and/or other relevant current data based on other data sample.

In a similar fashion, data sample 205f, 205h, 205i and 205j can be respectively with corresponding to track 201a, track 202a1, track 202b1 is related with the road segment segment on slope 204, because position that they are reported and orientation are corresponding to the position and the orientation in these roads or track.

Even if its reported position shown in outside the scope of road, data sample 205g also can be with related (for example corresponding to the road segment segment of track group 202b, the road segment segment that is used for HOV track 202b2), this is because reported position can be in the predeterminable range (for example 5 meters) of road.Alternatively, if the reported position of data sample 205b away from road, then it can be not related with any road segment segment yet.In certain embodiments, use different predeterminable ranges can for the data sample that provides by the different pieces of information source, so that the reflection data source is known or the accuracy level of expectation.For example, by the data sample that uses the mobile data source do not proofread and correct gps signal to provide can use high relatively (for example, 30 meters) predeterminable range, and can comparatively speaking use the predeterminable range of low (for example, 1 meter) by the data sample that usage variance is proofreaied and correct the mobile data source of GPS equipment and provided.

In addition, data sample filter can comprise identification not with the corresponding data sample of interested road segment segment and/or can not represent data sample in the actual vehicle of travels down.For example, can remove some data samples according to considering, because they are related with the irrespective road of data sample management system.For example, in certain embodiments, the data sample related with the road (for example, residential block street and/or arterial highway) of secondary function road class can be filtered.Return Fig. 2 A again, for example, can filtering data sample 205a and/or 205k, because road 203 is to be positioned at the local frontage road of low-down functional classification and not considered by the data sample management system, perhaps also can filtering data sample 205j, do not separate because onramp is too short with the expressway.Filtration can also be based on other factors, for example deduction in other mobile data source or report behavior on one or more road segment segment.For example, related with road segment segment and might represent that by a series of data samples of representing same position all that single mobile data source provides this mobile data source has stopped.If other data sample of all related with the same link section is all represented the mobile data source of moving, then the data sample corresponding to the mobile data source that stops can by filtering, be the vehicle that berths owing to the mobile data source for example owing to not being illustrated in the actual vehicle of travelling on this road segment segment.And, in certain embodiments, the report indication that data sample can comprise the vehicle traction state (for example, vehicle is transmitted as " parking " that engine starts, vehicle stops sending), if so, can use such indication to come the such data sample that can not represent the actual travel vehicle of filtering similarly.

Fig. 2 B illustrate with at specified time interval or obtain and the view of a plurality of data samples that road segment segment is related from a plurality of data sources in the section At All Other Times, wherein data sample marks on curve 210, and x axle 210b is the time of measuring, and y axle 210a is the speed of measuring.In this example, data sample shown in obtaining from a plurality of mobile datas source and one or more road traffic sensors related with road segment segment, and shown in legend in show (promptly with different shape, black solid diamond " ◆ " is used for from the data sample of road traffic sensors acquisition, and square hollow " " is used for from the data sample of mobile data source acquisition).As described in reference to figure 2A, the shown data sample that comes from the mobile data source can be related with road segment segment.

Exemplary data sample comprises road traffic sensors data sample 211a-c and mobile data source data sample 212a-d.Can determine the report speed of given data sample and writing time by its position on curve map.Mobile data source data sample 212d has the report speed of (or other speed unit) 15 mph.s and is recorded at about 37 minutes (or unit) At All Other Times with respect to some starting points.As the following more detailed description ground wanted, some embodiment can shown in analyze in the special time window in the time period or handle the data that obtained, for example time window 213.In this example, time window 213 comprises recorded data sample in 10 minutes the time interval of 30 minutes to 40 minutes time.In addition, some embodiment can also become two or more groups with the data sample component that produces in the special time window, for example, and group 214a and group 214b.For example, the data sample shown in should be noted in the discussion above that shows as the dual model (bi-modal) that has reflected report speed and distributes, and it has the bulk data sample, is reported in the speed in 25-30 mph. scope or the 0-8 mph. scope.May produce this dual model of speed or other multi-model (multi-modal) distribute be because, for example bottom magnitude of traffic flow pattern is non-homogeneous, here owing to for example make traffic to stop-the mobile traffic control signal of walking modes, or road segment segment comprises a plurality of traffic tracks of moving with friction speed (for example, HOV track or express lane have than other high relatively speed in non-HOV track).In this multi-model that has speed data distributes, some embodiment can be divided into two or more groups with data sample to be handled, so that processing degree of accuracy that generation improves or resolution are (for example, by calculating the average velocity that reflects each magnitude of traffic flow speed more accurately) and interested additional information is (for example, speed at HOV traffic and non-HOV traffic differences), or recognition data sample group is got rid of (for example, not comprising the part of HOV traffic as subsequent analysis).Though do not illustrate here, this different group of data sample can be discerned in every way, comprise by being the difference distribution modeling of every group of observation speed (for example normal or Gaussian distribution).

Fig. 2 C illustrates the example of filtrator execution data sample exceptional value being removed or considered the data sample eliminating that will not be illustrated in the up vehicle of sailing in particular lane highway section, it is based on the report speed that is used for data sample (though the one or more of data sample can replace with a part that performs an analysis in other embodiments, no matter and be to replace to get rid of the speed of being reported) in this example.Particularly, Fig. 2 C has shown table 220, and it illustrates for the example set execution data sample exceptional value of ten data samples and removes (quantity that is performed in actual use, the data sample of analysis can be bigger).Shown data sample is passable, for example, be all data samples that in special time window (for example time window 213 of Fig. 2 B), take place, or alternatively can comprise the subclass (for example included in the group 214a of Fig. 2 B or 214b) of the data sample of special time window or can comprise available all data samples in the longer time section.

In this example, in determined data sample group, the velocity deviation of coming each speed sample in the determining data sample group by the average velocity of other data sample from group is identified as statistics exceptional value with respect to other data sample with non-representational data sample.Can measure the deviation of each speed sample, the numerical value of the standard deviation that differs with respect to the average velocity of other data sample in group for example, the big data sample of its deviation ratio predetermined threshold (for example 2 standard deviations) is identified as exceptional value, and eliminating (for example, by abandoning) from further processing.

Table 220 comprises orientation row 222, and it has described the content of a plurality of row 221a-f.The every capable 223a-j of table 220 illustrates for the exceptional value of a different data sample in ten data samples and removes analysis, row 221a indicates to be the data sample of every row analysis, owing to will analyze the each row of data sample, therefore it be got rid of from other sample of this group to determine this result's difference.The data sample of row 223a can be referenced as first data sample, and the data sample of row 223b can be referenced as second data sample etc.Row 221b comprises the report speed of each data sample, and it with how many mph.s is measured.Row 221c has listed with respect to other data sample data sample of the given row that will be compared, in the group, and row 221d has listed the speed on a rough average by the data sample group of row 221c indication.Row 221e has comprised in the speed of the data sample of getting rid of from row 221b and has been listed in roughly deviation between the average velocity of other data sample the 221d, and it is measured with standard deviation.Whether big based on the deviation of in row 221e, listing than 1.5 standard deviations for this example purpose, be listed as 221f and indicate given data sample whether should be removed.In addition, the average velocity 224 that is used for all 10 data samples is shown as about 25.7 mph.s, and the standard deviation 225 of all 10 data samples is shown as about 14.2.

Like this, for example, the speed that row 223a illustrates data sample 1 is 26 mph.s.Next, the average velocity that calculates other data sample 2-10 is about 25.7 mph.s.Then the deviation of the average velocity of the speed of computational data sample 1 and other data sample 2-10 is approximately .02 standard deviation.At last, because the deviation of data sample 1 is lower than the threshold value of 1.5 standard deviations, so determining data sample 1 is not an exceptional value.In addition, the speed that row 223c illustrates data sample 3 is 0 mph., and the average velocity of other data sample 1-2 and 4-10 is calculated as about 28.6 mph.s.Then the deviation of the average velocity of the speed of computational data sample 3 and other data sample 1-2 and 4-10 is approximately 2.24 standard deviations.At last, because the deviation of data sample 3 is higher than the threshold value of 1.5 standard deviations, so determining data sample 3 is exceptional values.

More formally, given N data sample v ₀, v ₁, v ₂..., v _n, record and related with given road segment segment, current data sample v in the given time period _nTo be removed, if

$\frac{|v_i-\stackrel{\‾}{v_i}|}{{\mathrm{\σ}}_i}\≥c$

Wherein, v _iSpeed for analyzed current data sample;

Be other data sample (v ₀..., v _I-1, v _I+1..., v _n) average velocity; σ _iStandard deviation for other data sample; C is constant threshold (for example, 1.5).In addition, as the special circumstances of handling the division by 0 that may exist, if the standard deviation sigma of other data sample _iBe zero and the speed of current data sample and be not equal to other data sample

Average velocity, then remove current sample v _i

To each v _iBe noted that other data sample (v that might not want iteration all ₀..., v _I-1, v _I+1..., v _n) calculate on average

And standard deviation sigma _iOther data sample v ₀..., v _I-1, v _I+1..., v _nAverage

Also can followingly represent:

$\stackrel{\‾}{v_i}=\frac{N\stackrel{\‾}{v}-v_i}{N-1}$

And other data sample v ₀..., v _I-1, v _I+1..., v _nStandard deviation sigma _iCan followingly represent:

${\mathrm{\σ}}_i=\sqrt{\frac{1}{N-2}[(N-1){\mathrm{\σ}}^2-\frac{N{(v_i-\stackrel{\‾}{v})}^2}{N-1}]}$

Wherein, N is the sum (comprising current data sample) of data sample; V is all data sample v ₀, v ₁, v ₂..., v _nAverage; v _iBe the current data sample, and σ is all data sample v ₀, v ₁, v ₂..., v _nStandard deviation.By using above-mentioned formula, calculating mean value and standard deviation efficiently, and particularly can be with constant Time Calculation.Because above-mentioned algorithm has been calculated mean value and standard deviation for each data sample on each road segment segment, thus this rule operation O (MN) time, wherein M is the road hop count, N is the data sample number of each road segment segment.

In other embodiments, also can use other exceptional value to remove and/or data removal algorithm, can substitute or additional described exceptional value detection, for example based on neural network classifier, the nature Bayes classifier, and/or the regression model technology, and a plurality of data sample group is considered the technology of (for example, if at least some data samples are not independent with other data sample) together.

Fig. 2 D illustrates the example that uses data sample to carry out the average velocity estimation, and has shown the instance data sample that is used for particular lane highway section and time period that is similar to described in Fig. 2 B.Data sample marks in curve map 230, its in x axle 230b Measuring Time at y axle 230a measuring speed.In certain embodiments, the average velocity of given road segment segment can calculate by periodicity benchmark (for example, per 5 minutes).Each calculating can be in such as the schedule time window (or at interval) of 10 minutes or 15 minutes a plurality of data samples of consideration.If on such time window, calculate average velocity, for example at the terminal of time window or be bordering on the end, then when collecting the speed of data sample, the weighting in every way of data sample in time window, " age " of for example considering data sample (for example, based on to because the change of traffic, therefore older data sample is unlike in such intuition or the expection of precise information that the actual traffic situation of or other current times terminal about time window more can be provided like that near the newer data sample of current time place record, and older data sample is given a discount).Similarly, in certain embodiments, when the weighted data sample, can consider other data sample attribute, for example the type of data source or the particular source that is used for data sample are (for example, if data sample comes from more accurate or data source type or particular source than the better data of other data source can be provided than other data source, then just heavier to its weighting), and one or more other weighted factor type.

Shown in example in, the average velocity that is used for the example road segment segment calculated once on 15 minutes time window in per five minutes.This case description the relative weighting of two illustrated data sample 231a and 231b because they have contribution to two time window 235a and each average velocity that is calculated of 235b.Time window 235a is included in the data sample of record between 30 and 45 constantly, and time window 235b is included in the data sample of record between 35 and 50 constantly.Data sample 231a and 231b drop in time window 235a and the 235b.

Shown in example in, each data sample in the preset time window all with the proportional weighting of its age.That is to say that older data sample is than newer data sample weight less (therefore less to the contribution of average velocity).Particularly, in this example the weight of given data sample according to the age indication minimizing.The weighted function of this decay is by respectively corresponding to two weighting curve 232a and the 232b diagram of time window 235a and 235b.Each weighting curve 232a and 232b mark data sample writing time at x axle (level), mark weight at y axle (vertically).In time the sample weights of back (for example, more near the time window end) record greater than the sample of (for example, more beginning) record early in time near time window.The weight of given data sample can by on curve 230 from data sample paint downwards perpendicular line to it with corresponding to the place of weight map curve intersection of interested time window find out.For example, weight map 232a is corresponding to time window 235a, and according to the relative age of data sample 231a (older) and 231b (newer), the weight 233a of data sample 231a is less than the weight 233b of data sample 231b.In addition, weight map 232b is corresponding to time interval 235b, and similarly as can be seen the weight 234a of data sample 231a less than the weight 234b of data sample 231b.In addition, clearly, for the follow-up time window, the weight of given data sample decays in time.For example, the weight 233b of data sample 231b in time window 235a is greater than the weight 234b of the identical data sample 231b in time window 235b afterwards because data sample 231b during the time window 235a than during time window 235b, upgrading relatively.

More normally, in one embodiment, can followingly represent for weight with respect to the moment t recorded data sample of the time end at moment T place:

w(t)＝e ^-α(T-t)

Wherein, e is known mathematics constant, and α is variable parameter (for example, 0.2).More than given, then be N data sample v in time interval of T place end constantly ₀, v ₁, v ₂..., v _nWeighted mean velocity can followingly explain t wherein _iBe data sample v _iThe time (for example, its time that is recorded) of expression:

$\mathrm{Weihgted\; average\; speed}=\frac{\underset{i}{\overset{n}{\mathrm{\Σ}}}{v}_i{e}^{-\mathrm{\α}(T-t_i)}}{\underset{i}{\overset{n}{\mathrm{\Σ}}}{e}^{-\mathrm{\α}(T-t_i)}}$

And, the mistake of the average velocity that calculated is estimated and can followingly be calculated:

$\mathrm{Errore\; stimate}=\frac{\mathrm{\σ}}{\sqrt{N}}$

Wherein, N is data sample number and the σ data sample v for coming from average velocity ₀, v ₁, v ₂..., v _nStandard deviation.Also can determine the value of the confidence of other form in other embodiments similarly for the average velocity that calculates or produce.

As will be attentively, no matter substitute or except age of data sample, data sample can be based on the other factors weighting.For example, data sample can be as mentioned above but is used different weighting function (for example, the weight of data sample is with linear the minimizing rather than minimizing exponentially of age) to carry out time weight simultaneously.In addition, the data sample weighting can also based on the sum of the data sample in the interested time interval.For example, above-mentioned variable parameter α can depend on or based on the sum of data sample and change, so that the older more at most data sample of the quantity of data sample just (for example produces high more punishment, lower weight), the possibility that is more postponed the increase of (for example, newer) data sample to be reflected as the purpose of calculating average velocity.And data sample can be based on the other factors that comprises the data source type and weighting.For example, can be following situation, specific data source is (for example, specific road traffic sensors, or whole traffic sensors of particular network) all be that known (for example, based on the status information of reporting) or expectation (for example, based on history observation) are unreliable or coarse.Under these circumstances, the data sample (for example, the data sample 211a of Fig. 2 B) that obtains from such road traffic sensors can lack than the data sample weighting that from the mobile data source (for example data sample 212a of Fig. 2 B) obtains.

It is the example that road segment segment is carried out magnitude of traffic flow estimation that Fig. 2 E has simplified based on data sample, and it for example can comprise infers the volume of traffic, density and/or occupancy.In this example, the volume of traffic of given road segment segment is expressed as flowing through the vehicle total amount of road segment segment or the vehicle total amount that reaches on road segment segment in time window in given time window, the traffic density of given road segment segment (for example can be expressed as the per unit distance, mile or kilometer) the vehicle total amount, the traffic occupancy can be expressed as vehicle and take the particular lane highway section on the road segment segment or the mean time area of a room of point.

Given a plurality of different mobile data source of given road segment segment that will be observed during given time window, to travel, with as the known of total vehicle in mobile data source or expection number percent, then can infer total volume of traffic---the vehicle fleet (comprising the vehicle that is not the mobile data source) of the road segment segment of during time window, travelling.Average velocity from the estimation of total volume of traffic of being inferred and the vehicle on road segment segment just can further calculate traffic density and road occupancy.

A kind of simple approach of estimation total volume of traffic in particular lane highway section during the special time window is to remove the quantity in the mobile data source of this time window with the number percent of the actual vehicle of expecting to become the mobile data sample source simply---like this, for example, if will become the mobile data sample source from 25 mobile data sources reception mobile data samples and at 10% of the total vehicle of road segment segment expection in time window, then the total amount of estimating for the time quantum of this time window is 250 actual vehicle.But because the intrinsic changeability of vehicle arrival rate, if particularly the expectation number percent of mobile data sample source is very little, then this approach may cause the great variety of adjacent time window total amount estimation.As a kind of replacement, it provides more complicated analysis, and total volume of traffic of given road segment segment can followingly be inferred.The different mobile data source of given specific quantity (for example, each vehicle) n on the road segment segment of length 1, in given time period τ, uses Bayesian statistics to infer the main average rate (underlyingmeans rate) that the mobile data source arrives, λ.The mobile data source that arrives on one section road corresponding to road segment segment can stochastic modeling, and therefore discrete processes can be described by Poisson statistics, that is: on time

$p\left(n\right|\mathrm{\λ})=\frac{{\mathrm{\λ}}^n{e}^{-\mathrm{\λ}}}{n!}$

From above formula, can calculate the possibility that n mobile data source is observed, given mean arrival rate λ and the vehicle number n that is observed.For example, assumed average arrival rate λ=10 (vehicle/unit interval) and observation n=5 portion vehicle, then replace generation:

$p\left(n\right|\mathrm{\&lambda;})=\frac{{10}^5{\mathrm{<figure-callout\; id="10"\; label="e"\; filenames="A200780015916E01231.png,A200780015916E01251.png"\; state="\{\{state\}\}">e</figure-callout>}}^{10}}{5!}\&ap;0.038$

Expression actual observation n=5 portion vehicle has 3.8% possibility.Similarly, if being λ=10 (vehicle/unit interval) then actual observation to 10 vehicle, mean arrival rate reaches that (that is, possibility n=10) is 12.5%.

Above formula can make the possibility of the specific arrival rate λ that is used for determining the given n of observation with Bayes' theorem.As known, Bayes' theorem is:

$p\left(\mathrm{\&lambda;}\right|n)=\frac{p\left(n\right|\mathrm{\&lambda;})p\left(\mathrm{\&lambda;}\right)}{p\left(n\right)}$

By replacing and the constant removal, can obtain as follows:

$p\left(\mathrm{\&lambda;}\right|n)\&Proportional;\frac{{\mathrm{\&lambda;}}^n{e}^{-\mathrm{\&lambda;}}}{n!}$

From above, the proportional or relative possibility of arrival rate λ can be calculated in n mobile data source of given observation, and the probability distribution of the probable value of λ is provided when each observed reading of given n.For the particular value of n, the degree of confidence that the possibility on each arrival rate value distributes and allows to select a representational arrival rate value (for example, mean value or intermediate value) and allow this value of estimation.

And, give the known percentage fix on the road as total vehicle in mobile data source, also as " permeability factor ", the arrival rate amount of therefore can following calculating total traffic:

$\mathrm{Total\; traffic\; volume}=\frac{\mathrm{\&lambda;}}{q}$

In certain embodiments, the total volume of traffic on the road segment segment alternatively can be expressed as the total amount k of vehicle that flows through the length 1 of road segment segment at time τ in the time period.

Fig. 2 E illustrates given observation sample size, the probability distribution of various total volume of traffic of given sample mobile data source permeability factor q=0.014 (1.4%).Particularly, Fig. 2 E illustrates three-dimensional curve diagram 240, and it has marked the mobile data source number (n) that observes on y axle 241, has indicated the traffic arrival rate amount of inferring on x axle 242, and has indicated the possibility of the traffic value of each deduction on z axle 243.For example, this curve map shown given mobile data source observation count n=0, the actual traffic amount is about 0.6 (or 60%) near the possibility zero, as by shown in the hurdle 244a, and time per unit actual traffic amount is about 0.1 in the possibility of 143 left and right vehicle wheels, shown in hurdle 244b.And, given mobile data source observation count n=28, then the total actual traffic amount of time per unit at 2143 left and right vehicle wheels (corresponding to about 30 the mobile data sample sources of time per unit, the permeability factor of given example) possibility is about 0.1, shown in hurdle 244c, it has shown the intermediate value that approaches total actual traffic amount.

In addition, can use total traffic arrival rate amount (being illustrated in the vehicle number k that arrives in the time τ of road segment segment) of the deduction that is used for given road segment segment, the average velocity v that is estimated and average vehicle length d to calculate average occupancy and density, then

$\mathrm{Vehicles\; per\; mile},m=\frac{k}{\mathrm{v\&tau;}}$

Occupancy＝md

As discussed previously, the average velocity v of the vehicle on road segment segment can obtain by the operating speed estimating techniques, for example the description of being done with reference to figure 2D.

Figure 10 A-10B illustrates the example of adjusting or revising from the misdata sample of for example unreliable and obliterated data sample of road traffic sensors etc.Particularly, Figure 10 A has shown a plurality of instance data readings that obtain from a plurality of traffic sensors in each time, and it is organized in the table 1000.Table 1000 comprises a plurality of data readings row 1004a-1004y, its each comprise that unique identification provides traffic sensor ID (" the identifier ") 1002a of the traffic sensor of reading, traffic sensor data readings value 1002b comprises the traffic flow information by the traffic sensor report, the traffic sensor reading duration, 1002c reflected the time by traffic sensor image data reading, and traffic sensor state 1002d comprises the indication of traffic sensor mode of operation.Though traffic sensor can be reported the traffic flow information (for example, the volume of traffic and occupancy) of other type in other embodiments, has only shown velocity information in this example, and value also can be with other form report.

Shown in example in, data readings 1004a-1004y can each time by a plurality of traffic sensor collections and can being shown in the table 1000 by record sheet.In some cases, data readings by traffic sensor periodically (for example, per minute, per five minutes etc.) gather and/or with such cycle by this traffic sensor report.For example, traffic sensor 123 per five minutes image data readings, shown in data reading 1004a-1004d and 1004f-1004i, it has shown a plurality of data readings of being gathered independently two days (being 8/13/06 and 8/14/06) at 10:25AM and 10:40AM by traffic sensor 123 in this example.

Data readings 1004a-1004y shown in each comprises data readings value 1002b, and it comprises the traffic flow information of being observed or being obtained by data transducer.Such traffic flow information can comprise the arrival of travelling, close on or pass through one or the speed of multi-section vehicle of traffic sensor.For example, data readings 1004a-1004y has shown the car speed that sensor 123 arrives at four different time observations respectively, 34 mph.s (mph), 36mph, 42mph and 38mph.In addition, traffic flow information can comprise the arrival of travelling, close on or the vehicle total amount by traffic sensor or increase progressively counting, and no matter substitutes or except speed and/or out of Memory.Total quantity can be when traffic sensor is mounted or activate, the semi-invariant of the vehicle of traffic sensor observation.Increase progressively counting and can be from sensor acquisition formerly during data readings, by the semi-invariant of the vehicle of traffic sensor observation.Data readings 1004w-1004x has shown at two different timers 166 and has added up 316 cars and 389 cars respectively.In some cases, the recorded data reading can not comprise the data readings value, for example work as given traffic sensor and sensor fault occurred, thus can not collection or hourly observation or report observation (for example, because network failure).For example, data readings 1004k has shown that traffic sensor 129 can not provide the data readings value at the 10:25AM of 8/13/06 this day, as indicated in data readings value row 1002b by "--".

In addition, traffic sensor state 1002d can be related with at least some data readings, if for example traffic sensor and/or corresponding communication network provide the indication of the mode of operation of this traffic sensor.In an illustrated embodiment, mode of operation comprises that sensor function (for example indicates normally, OK), sensor off-position (for example, OFF) indication, sensor (is for example handled the single value of report, STUCK) indication, and/or disconnect (COM_DOWN) indication with the communication link of network is as respectively at data readings 1004m, 1004k is shown in 1004o and the 1004s.In other embodiments, other and/or different information of the mode of operation that relates to traffic sensor can also be provided, perhaps this operational status information can be mustn't go to.Other traffic sensor, for example traffic sensor 123 and 166 is not configured to provide traffic sensor state indication in this embodiment, as among the traffic sensor status Bar 1002d shown in "--".

Row 1004e, 1004j, 1004n, 1004q, 1004v and 1004y and row 1002e point out to write down additional traffic sensor data readings in certain embodiments and/or can provide additional information also/or it is recorded as the part of each data readings.Similarly, in certain embodiments, information is lacking of showing than using described technology here.

Figure 10 B illustrates the example of the mistake in the traffic sensor data readings that detects the unsound traffic sensor that expression can not correctly work.Particularly, because a lot of traffic sensors can not provide the indication of traffic sensor state, and since in some cases the indication of such traffic sensor state may be insecure (for example, the indication sensor function is undesired but in fact it is normal, or the indication sensor function normally but in fact it is undesired), therefore may need to use statistics and/or other technology to detect unsound traffic sensor based on the data readings value of being reported.

For example, in certain embodiments, unsound traffic sensor can by will by the time period of given traffic sensor in certain day (for example, at 4:00PM and 7:29PM) in the history of the data readings reported in same time period of (for example, 120 of the past days) in the past several days of the current distribution of the data readings reported and this sensor distribute and compare and detect.Such distribution can produce by for example handling a plurality of data readings that obtain from all traffic sensors as shown in Fig. 10 A.

Figure 10 B has shown three histograms 1020,1030 and 1040, and its each expression is based on the data readings distribution of the data readings that is obtained from traffic sensor 123 in the interested time period of institute.At histogram 1020, the interval that the data of expression are dispersed to 5 mph.s in 1030 and 1040 (for example, 0 to 4 mph., 5 to 9 mph.s, 10 to 14 mph.s etc.) and standardization, so that every hurdle (for example the hurdle 1024) representative occurs in this time period (number percent of data readings in for example, based on the time period that is falling in this barrel) probability of inherent 0 and 1 for the car speed of this hurdle car speed in 5 mph. buckets (bucket).For example, the car speed that hurdle 1024 is illustrated between 50 and 54 mph.s is observed by traffic sensor 123, about 0.23 probability is arranged, for example based on the report speed that has about 23% (containing) of the data readings that obtains from traffic sensor 123 between 50 and 54 mph.s.In other embodiments, can use one or more other barrel sizes, and no matter or replace the bucket of 5mph.For example, the 1mph bucket can provide thinner processing at interval, if but in the time period, can not obtain sufficient data readings, then also may cause the great variety between adjacent bucket, and the 10mph bucket can provide less variation but details is also few.In addition, though current example uses average velocity as the measuring of data readings analysis and comparison, other embodiment also can use one or more replacements or except that average velocity other to measure.For example, at least some embodiment, can use the volume of traffic and/or occupancy similarly.

In this example, histogram 1020 has represented that in the past the history of the data readings that by traffic sensor 123 gathered between 9:00AM to 12:29PM 120 days Monday distributes.The distribution of the data readings that histogram 1030 expression is just often gathered by sensor 123 between the 9:00AM to 12:29PM of specific Monday when sensor 123 functions.Can find out clearly that the shape of histogram 1030 and histogram 1020 are similar, suppose in the expection of the travel pattern of specific Monday similarly, then will discuss, can calculate similar degree in every way as following with the travel pattern of general Monday.The distribution of the data readings that histogram 1040 expressions are gathered by traffic sensor 123 between the 9:00AM to 12:29PM of specific Monday when sensor 123 functions are undesired, and export the data readings that can not reflect the actual traffic flow on the contrary.As obviously find out ground, the shape of histogram 1040 is different with histogram 1020 significantly, and it has reflected the data readings by the mistake of traffic sensor 123 reports.For example, projection huge in this distribution can find out in hurdle 1048, and sensor 123 had been stuck and has reported a large amount of constant reading that can not reflect the actual traffic flow when it may be illustrated between 9:00AM to 12:29PM at least some.

In certain embodiments, determine similarity between two distributions though can use in the Kullback-Leibler divergence (divergence) between two traffic sensor DATA DISTRIBUTION, similarity or the difference between distributing also can otherwise be calculated in other embodiments.The Kullback-Leibler divergence is that the convexity of the similarity of two probability distribution P and Q is measured.It can followingly be represented:

$D_{\mathrm{KL}}\left(P\right|\left|Q\right)=\underset{i}{\mathrm{\&Sigma;}}{P}_i\log \left(\frac{P_i}{Q_i}\right)$

Wherein Pi and Qi are the value (for example, each Pi and Qi are that speed appears at i the probability in the bucket) of discrete probability distribution P and Q.Shown in example in, the data readings shown in the histogram 1020 distribute and the Kullback-Leibler divergence (" DKL ") 1036 that between the data readings shown in the histogram 1030 distributes, is used for healthy traffic sensor for about 0.076, and distribute and the Kullback-Leibler divergence 1046 that is used for unsound traffic sensor between the data readings shown in the histogram 1040 distributes is about 0.568 in the data readings shown in the histogram 1020.As possibility is desired, DKL 1036 is significantly less than DKL 1046 (in this case, be approximately DKL 1046 13%), it (has for example reflected histogram 1030, be illustrated in the output of traffic sensor 123 just often of its function) similar in appearance to histogram 1020 (for example, the average behavior of expression traffic sensor 123) is far more than that more histogram 1040 (traffic sensor 123 when for example, being illustrated in its fault) is similar in appearance to histogram 1020.

In addition, substitute such as the similarity of coming and measure or in addition, some embodiment can use other statistics to measure the misdata reading that is provided by traffic sensor, for example statistical information entropy are provided from the Kullback-Leibler divergence.The statistical entropy of probability distribution is the measuring of otherness of probability distribution.The statistical entropy of probability distribution P can followingly be represented:

$H\left(P\right)=-\underset{i}{\mathrm{\&Sigma;}}{P}_i\log P_i$

Wherein, Pi is the value (for example, each Pi is the interior probability of i bucket that speed drops on the P histogram) of discrete probability distribution P.In an illustrated embodiment, statistical entropy 1022 in the distribution shown in the histogram 1020 is approximately 2.17, statistical entropy 1032 in the distribution shown in the histogram 1030 is approximately 2.14, and is approximately 2.22 in the statistical entropy 1042 of the distribution shown in the histogram 1040.As may be expectedly, statistical entropy 1042 be all bigger than statistical entropy 1032 and statistical entropy 1022, and this has reflected that traffic sensor 123 has been showed chaotic more output mode when its fault.

In addition, the difference between two statistical entropies are measured can be measured by calculating the entropy difference measurement.Entropy difference measure between two probability distribution P and Q can followingly be represented:

EM＝‖H(P)-H(Q)‖ ²

Wherein H (P) and H (Q) are respectively the entropy of probability distribution P and Q as mentioned above.Shown in example in, be approximately 0.0010 in distribution shown in the histogram 1020 and the entropy difference measure between the distribution shown in the histogram 1030 (" EM ") 1034, and be approximately 0.0023 in distribution shown in the histogram 1020 and the entropy difference measure 1044 between the distribution shown in the histogram 1040.As can be expectedly, that the obvious specific entropy difference measure 1034 of entropy difference measure 1044 are wanted is big (in this situation big twice), and this has reflected the statistical entropy of the distribution shown in the histogram 1040 and has wanted big in the difference between the statistical entropy of the distribution shown in the histogram 1020 than statistical entropy and the difference between the statistical entropy of the distribution shown in the histogram 1020 in the distribution shown in the histogram 1030.

Can use above-mentioned statistics to measure in every way and detect unsound traffic sensor.In certain embodiments, the various information that relevant current data reading distributes can be provided as the input to sensor health (or data readings reliability) sorter, for example based on neural network, Bayes classifier, decision tree etc.For example, the sorter input information can comprise, for example, and the statistical entropy that Kullback-Leibler divergence between the historical data reading that is used for this traffic sensor distributes and the current data reading that is used for this path sensor distributes and current data reading distribute.Then, sorter is estimated the health of this traffic sensor based on the input that is provided, and the output of expression health or unhealthy sensor is provided.In some cases, also provide additional information to be used as the input of sorter, for example the indication of the time in one day (for example, time period from 5:00AM to 9:00AM), the indication of in one week certain day or a few days (for example, from the Monday to the Thursday, Friday, Saturday or Sunday) and/or distribute corresponding to current and historical data reading one day in time or certain day in the week, the size of mph group etc.Sorter can be trained by using actual past data reading, such as the expression that comprises the traffic sensor state, just as shown in Fig. 10 A.

In other embodiments, unsound traffic sensor need not to use sorter just can be identified.For example, if one or more statistics is measured greater than predetermined threshold value, can determine that then traffic sensor is unsound.For example, if the historical data reading that is used for traffic sensor distribute and the current data reading that is used for this path sensor Kullback-Leibler divergence between distributing greater than first threshold, if the statistical entropy that the current data reading distributes is greater than second threshold value, if and/or the entropy difference measure between distribution of current data reading and the distribution of historical data reading can determine then that greater than the 3rd threshold value this traffic sensor is unsound.In addition, also can use other non-statistical information, whether report such as traffic sensor to be considered to healthy or unsound sensor states.

Will be attentively as previous institute, though above-mentioned technology mainly is described in the context of the traffic sensor of reporting vehicle velocity information, same technology also can be used other traffic flow information, comprise the volume of traffic, density and occupation rate.

Fig. 3 is some the structural drawing of embodiment of computing system 300 that diagram is suitable for carrying out described at least technology, for example by carrying out the embodiment of data sample management system.Computing system 300 comprises CPU (central processing unit) (" CPU ") 335, each I/O (" I/O ") assembly 305, storer 340 and internal memory 345, and shown I/O assembly comprises display 310, network connects 315, computer-readable medium drive 320 and other I/O equipment 330 (for example, keyboard, mouse or other optional equipment, microphone, loudspeaker etc.).

In an illustrated embodiment, in internal memory 345, carry out some that various systems carry out described at least technology, comprise that data sample management system 350, predicted traffic information provide system 360, key road identifier system 361, road segment segment that system 362, RT information providing system 363 and other the optional system that is provided by program 369 are provided, these various executive systems all are referred to as traffic information system usually here.Computing system 300 and its executive system can be via network 380 (for example, internet, one or more mobile telephone networks etc.) communicate by letter with other computing system, for example each client device 382, based on client and/or data source 384, road traffic sensors 386, other data source 388 and third party's computing system 390 of vehicle.

Particularly, data sample management system 350 obtains the information of the situation data of various relevant current traffic condition and/or previous observation from each source, for example from road traffic sensors 386, based on the mobile data source 384 of vehicle and/or other moves or non-moving data source 388 obtains.Then data sample management system 350 is by (for example filtering, consider to remove data sample) and/or (for example adjust, error recovery) data are come the data preparing to obtain for the use of other assembly and/or system, then use the data of being prepared to estimate the road traffic condition of each bar road segment segment, for example magnitude of traffic flow and/or speed.Among the embodiment shown in this, data sample management system 350 comprises data sample filter assemblies 352, sensing data is adjusted assembly 353, the data sample exceptional value is removed assembly 354, data sample velocity estimation assembly 356, data sample flow estimation assembly 358 and optional sensor data collection assembly 355, wherein assembly 352-358 carries out and to be similar to the described function of the corresponding assembly of front in Fig. 1 (for example, the data sample filter assemblies 104, sensing data is adjusted assembly 105, the data sample exceptional value is removed assembly 106, data sample velocity estimation assembly 107, data sample flow estimation assembly 108 and optional sensor data collection assembly 110).In addition, at least some embodiment, the data sample management system with basic in real time or the mode of near real time carry out the estimation of road traffic condition, for example in a few minutes, obtain bottom data (himself can obtain in real-time substantially mode from data source).

Other traffic information system 360-363 and 369 and/or third party's computing system 390 then can use the data that provide by the data sample management system in every way.For example, predicted traffic information provides system 360 can obtain (directly, or indirectly via database or memory device) this data of preparing to be to produce further traffic condition predictions in a plurality of following times, and information of forecasting offered one or more other receiving ends, for example one or more other traffic information systems, client device 382 is based on client 384 and/or third party's computing system 390 of vehicle.In addition, RT information providing system 363 can obtain the information of the relevant road traffic condition of being estimated from the data sample management system, and with road traffic condition information with in real time or be bordering on real-time mode and (for example offer its side, client device 382, client 384 and/or third party's computing system 390 based on vehicle)---when the data sample management system also with this in real time or when being bordering on real-time mode and carrying out estimation, can be from the take over party of the next data of RT information providing system based on browsing at the actual vehicle travel conditions of the same period on one or more road segment segment and the information of using relevant current traffic condition on these road segment segment (as what reported by mobile data source of travelling in these road segment segment and/or sensor, and other data source provides the information of relevant actual vehicle travel conditions on these road segment segment).

Client device 382 can adopt various forms in each embodiment, and can comprise that usually any communication facilities and other can produce any computing equipment of asking to give traffic information system and/or receiving information from traffic information system.In some cases, client device (for example can be carried out the operable mutual controlling application program of user, Web browser) (for example to produce to the request of the information that relates to traffic, the future transportation condition information of prediction, in real time or be bordering on real-time current traffic condition information etc.), and in other situations, the information that relates to traffic that at least some are such can automatically (for example be sent to client device from one or more traffic information systems, text message, the new Web page, specific routine data renewal etc.).

Road traffic sensors 386 comprises in a plurality of each street, expressway or other roads that are installed in such as one or more geographic areas, goes up or near sensor.These sensors can comprise loop sensor, the speed of the quantity of energy measurement time per unit by the vehicle of these sensors, vehicle and/or relate to other data of the magnitude of traffic flow.In addition, such sensor can comprise camera, motion sensor, radar ranging equipment, based on the equipment of RFID be positioned at the next-door neighbour or near the sensor of other type of road.Road traffic sensors 386 can be periodically or continuously by based on wired or by the network 380 that uses one or more data exchange mechanisms (for example, push away, draw, token, request-reply, point-to-point etc.) data readings of measuring is offered data sample management system 350 based on wireless data link.In addition, though do not illustrate here, but in certain embodiments, one or more gatherers of such road traffic sensors information (for example, the traffic department of government of operation sensor) can replace and obtain raw data and make data is available (no matter be original form or in its processed back) to traffic information system.

Other data source 388 comprises polytype other data source, and it can be made by one or more traffic information systems and be used for providing the information of relevant traffic to user, consumer and/or other computing system.Such data source comprises Map Services and/or the database that relevant road network information can be provided, for example each other connective of each bar road and the traffic control signal (for example, the existence and the position in traffic control signal and/or speed limit district) that relates to such road.Other data source can also comprise the source about the information of the incident of influence and/or reflection traffic and/or situation, arrange for example short-term and long-range weather forecasting, school's schedule and/or calendar, schedule of events and/or calendar, the traffic accident report that provides by manual operation person (for example, first present members, law enfrocement official, expressway employee, news media, tourist etc.), road job information, holiday etc.

In this embodiment each can be to be positioned at vehicle data are offered one or more traffic information systems and/or receive data computing system and/or communication systems from one or more these systems based on the clients/data sources 384 of vehicle.In certain embodiments, data sample management system 350 can be used mobile data source and/or other distributed network based on user's mobile data source (not shown) based on vehicle that the information that relates to current traffic condition is provided as the use of traffic information system.For example, every vehicle or other mobile data source can have GPS (" GPS "), and equipment (for example, have the mobile phone of GPS function, GPS equipment etc. independently) and/or other can determine the geolocation device in geographic position, and may also have out of Memory, for example speed, direction, height above sea level and/or other relate to the data of vehicle ', and geolocation device or other different communication facilities obtain sometimes and provide such data to one or more traffic information systems (for example, passing through Radio Link).Such mobile data source will discuss in more detail elsewhere.

Alternatively, based on the clients/data sources 384 of vehicle some or all each can have the computing system that is positioned at vehicle and/or communication system with from one or more traffic information system acquired informations, for example for vehicle user's use.For example, vehicle can comprise the Web browser with installation or embedded panel board (in-dash) navigational system of other controlling application program, the user can use this system to come from one of traffic information system (for example predicted traffic information provides system and/or RT information providing system) request traffic relevant information, and perhaps these requests can be sent by the portable set of the user in the vehicle.In addition, one or more traffic information systems can be based on the reception of lastest imformation or produce automatically will be referred to traffic information transmission to such client device based on vehicle.

Third party's computing system 390 comprises one or more optional computing systems, and it is by such as a side of the data that receive relevant traffic from one or more traffic information systems with use operator's operation of other people rather than traffic information system of the side etc. of data in some way.For example, third party's computing system 390 can be such system, it is from one or more traffic information system receiving traffic informations, and related information (no matter being the out of Memory that the information that received also is based on the information that is received) offered user or other people (for example, by Web inlet or subscription service).Alternatively, third party's computing system 390 can be operated by a side of other type, for example collect and report the media organization of traffic, or provide the information of relevant traffic to be used as the Online Map company of an itinerary service part for their user to the consumer.

Will be attentively as front institute, the data that predicted traffic information provides system 360 to use to be prepared by data sample management system 350 and other assembly in the embodiment shown are to produce the traffic condition predictions in future of a plurality of future times.In certain embodiments, the probability technology has been used in the generation of forecast, its merged that various types of input data think many road segment segment each produce a series of future times forecasts repeatedly, for example based on the changing the present situation of the road network in given geographic area and in real-time mode.And, in at least some embodiment, automatically creating one or more predictability Bayes or other model (for example, decision tree) in giving the future transportation condition predicting of each interested geographic area, using, for example based on the historical traffic of being observed of these geographic areas.The future transportation condition information of predictability can use in every way helping travelling or other purpose, so that based on the prediction plan of the traffic of the roads of a plurality of following times optimal route by road network.

And, road segment segment determine system 362 can use provide the Map Services that relates to the information of road network in one or more geographic areas and/or database with determine automatically and management relate to may be by the information of the employed relevant road of other traffic information system.The information of relevant road like this can comprise (for example the determining of specific part of the road that will be used as interested road segment segment, traffic based on these road parts and other adjacent road part), and (for example in the interested out of Memory indication of the road segment segment of given road network and institute, the physical location of road traffic sensors, case point, terrestrial reference; Information about function road class and other relevant traffic characteristic; Deng) between the related or relation that automatically produces.In certain embodiments, road segment segment determine that system 362 can periodically carry out and for the use of other traffic information system in storer 340 or database (not shown) its information of producing of storage.

In addition, key road identifier system 361 uses the given geographic area of expression and is used for the road network of the traffic related information of that geographic area, thinking tracking and estimation road traffic condition and discern interested road automatically, for example is the use of other traffic information system and/or traffic data client.In certain embodiments, the automatic identification of interested road (or one or more road segment segment of road) can be at least in part based on following factor, the value of the peak value volume of traffic or other flow for example, the value of peak value traffic congestion, changed the same day of the volume of traffic or other flow, changed the same day of congestion in road, and (inter-day) in the daytime of the volume of traffic or other flow changes, and/or the variation in the daytime of congestion in road.Such factor can be analyzed by for example primary clustering (principal component), for example, then calculate the eigen decomposition of covariance matrix S by at first calculating the covariance matrix S of the traffic related information that in given geographic area, is used for all roads (or road segment segment).Then in the descending of eigenwert, the eigenvector of S represents that independently the variation to the traffic of being observed has the combination of the road (or road segment segment) of the strongest contribution.

In addition, Real-time Traffic Information provides or presents system can perhaps alternatively be provided by one or more other programs 369 by the RT information providing system.Information providing system can use by data sample management system 350 and/or other assembly (for example predicted traffic information provides system 360) and analyze and the data that provide are come for operation or used client device 382, provide traffic-information service based on the consumer and/or the commercial entity of the client 384 of vehicle, third party's computing system 390 etc., so as at least in part based on the data sample that obtains from vehicle and other mobile data source with in real time or be bordering on real-time mode data are provided.

Can predict, shown computing system only is schematically and not to attempt to limit the scope of the invention.Computing system 300 can be connected with other unshowned equipment, comprises by the network of one or more for example internets or via Web.In general, " client " or " server " computing system or equipment, or traffic information system and/or assembly, can comprise can be mutual and carry out the combination in any of the hardware and software of described type of functionality, include but not limited to desktop or other computing machine, database server, the network storage equipment and other network equipment, PDA, cellular mobile phone, wireless telephone, beeper, communicator, internet application, based on the system of TV (for example, using set-top box and/or individual/digital video recorder) with comprise various other consumer products with suitable interactive communication ability.In addition, in certain embodiments by shown in the function that provides of system component can be integrated in still less the assembly or be distributed in the additional assembly.Similarly, the function of some in the assembly shown in certain embodiments can not be provided and/or can obtain other additional function.

In addition, can be stored in storer or the memory storage though various projects are as directed when using, for the purpose of memory management and/or data integrity, these projects or their part can be transmitted between storer and other memory device.Alternatively, in other embodiments some or all of component software and/or module can in the storer on another equipment, carry out and by the communication of intercomputer with shown in computing system communicate by letter.Some or all of system component or data structure also (for example can be stored in computer-readable medium, as software instruction or structural data), for example by suitable driver or the hard disk that reads by suitable connection, storer, network or portable media medium.System component and data structure (for example also can be transmitted as the data-signal that produced on various computer-readable transmission mediums, part as carrier wave or other analog or digital transmitting signal), comprise based on wireless and based on the medium of wired/cable, and (for example can adopt various forms, as the part of single or multiplexing simulating signal, or as a plurality of discrete digital packets or frame).In other embodiments, such computer program can also adopt other form.Therefore, the present invention also can realize with other Computer Systems Organization.

Fig. 4 is the process flow diagram of the exemplary embodiment of data sample filtrator routine 400.This routine can be provided by the execution of the embodiment of the data sample filter assemblies 104 of the data sample filter assemblies 352 of for example Fig. 3 and/or Fig. 1, so that receive data sample, and filter out uninterested data sample for the estimation of back corresponding to road in the geographic area.The data sample that filters then can use in every way subsequently, for example uses the data sample that filters to calculate the average velocity in institute interested particular lane highway section and calculates other feature about the magnitude of traffic flow for such road segment segment.

Routine is that the geographic area of special time period receives the data sample group in step 405 beginning here.In step 410, some or all generation additional informations that routine is these data samples based on other relevant data sample alternatively then.For example, lack institute's information of interest (for example speed in mobile data source and/or orientation or direction) if be used for the particular data sample in vehicle or other mobile data source, then such information can be in conjunction with the previous and subsequent data sample in identical mobile data source one or both of is determined.In addition, in at least some embodiment, can collect the information that is used for specific mobile data source of coming from a plurality of data samples and estimate additional information type about this data source, so that estimation is across the behavior of the data source in the time period of a plurality of data samples (for example, determine whether that vehicle has stopped a few minutes rather than temporarily stop to be used as in one or two minute the normal wagon flow of traffic, for example meets stop sign or stop light).

After step 410, though it is related with the particular lane highway section of road in this geographic area and this road to attempt each data sample that routine proceeds to step 415, but this step can not be performed or otherwise carry out in other embodiments, if for example the initial association of data sample and road and/or road segment segment receives in step 405 at least, if or alternatively whole routine is next corresponding to a road segment segment thereby all data samples that receive in step 405 are organized as one at a road segment segment execution one time.In an illustrated embodiment, data sample can be carried out in every way with the related of road and road segment segment, for example carry out initial association (for example, that data sample is related with nearest road and road segment segment) based on the geographic position related separately with this data sample.And, this association can comprise alternatively that additional analysis is with concise or revision initial association---for example, (for example many road segment segment are used for a specific road if location-based analysis indication has a plurality of possible road segment segment for data sample, or alternatively many road segment segment are used to close on but incoherent road), then such analyzing adjuncts can use the out of Memory such as speed and direction to influence related (for example, merging positional information and one or more other such factor by the mode with weighting).Like this, for example, if the reported position of data sample is between expressway and adjacent frontage road, then just can use the information of the speed of reporting of relevant data sample to help with this data sample related with suitable road (for example, by determining to come from frontage road) with 25 mph. speed limits with the data sample of the velocity correlation of 70 mph.s.In addition, in the certain extension of road or other road part and many different road segment segment (for example, road for two-way traffic, travelling and be modeled as first road segment segment and travelling on another direction is modeled as the second different road segment segment in one direction wherein, or alternatively for the expressway of multilane, the HOV track is modeled as and one or more adjacent non-HOV track road segment segment independently) under the situation about being associated, can use the road segment segment of selecting most possible road for this data sample such as the additional information of relevant data samples such as speed and/or direction.

After step 415, routine proceed to step 420 think follow-up processing filter out not with the related any data sample of interested road segment segment, comprise not related data sample (if there is) with any road segment segment.For example, the part of specified link or road may not be that subsequent analysis institute is interested, the road of for example getting rid of specific function road class (for example, can be interested to some extent if the size of road and/or the volume of traffic are not large enough to), or because such as ramp, expressway or special-purpose road or cross/traffic characteristic of such road part such as minute cross road can not reflect expressway as a whole, therefore get rid of such road part.Similarly, under many road segment segment situation related with the specific part on road, some road segment segment are may some purpose institute interested, if for example having only the behavior in non-HOV track is that specific purpose institute is interested, if or to have only a direction be interested in the track of both direction, then is that the HOV track is got rid of in the expressway.Though after step 420, routine proceeds to step 425 to determine whether the behavior filtering data sample based on data source, so in other embodiments filtration also can not be performed or also can carry out always.In an illustrated embodiment, if filtration is carried out in the behavior based on the source, then routine proceeds to step 430 to carry out such filtration, for example remove can not reflect the data source of wanting measured interested magnitude of traffic flow behavior corresponding to its behavior data sample (for example, eliminating engine in the time expand section is starting the vehicle that stops, and gets rid of the vehicle that spins in stop ground or parking lot or other zonule in the time period that prolongs etc.).After step 430, if or alternatively in step 425, determine not filter based on the behavior of data source, then routine proceeds to the data that step 490 is thought follow-up use stored filter, but the data of filtering in other embodiments alternatively can directly offer one or more clients.Then routine proceeds to step 495 to determine whether continuation.If continue, then routine turns back to step 405, if do not continue, then arrives step 499 and end.

Fig. 5 is the process flow diagram of the exemplary embodiment of data sample exceptional value remover routine 500.This routine can be removed the embodiment of assembly 106 and provided by the data sample exceptional value that the data sample exceptional value of for example execution graph 3 is removed assembly 354 and/or Fig. 1, is the data sample of exceptional value thereby remove for this road segment segment with respect to other data sample of road segment segment.

This routine receives the one group of data sample that is used for road segment segment and time period therein in step 505 beginning.The data sample that is received can be, for example the data sample of the filtration that obtains from the output of data sample filtrator routine.In step 510, routine then is divided into data sample a plurality of groups alternatively with reflection different part of road segment segment and/or different behavior.For example, if a part and these many tracks that track, many expressways is included in together as the single road section comprise at least one HOV track and one or more non-HOV track, if the magnitude of traffic flow then in the time period is significantly different between HOV and non-HOV track, then can separate with the vehicle on other track at the vehicle on the HOV track.Can carry out such grouping in every way, for example data sample be fitted to many curves, every curve is represented the typical data sample changed (for example, normal state or Gaussian curve) in the particular data sample group.In other embodiments, also can not carry out such grouping, all reflect similar behavior (for example, alternatively being split into many road segment segment) if for example alternatively cut apart road segment segment for use in all data samples of this road segment segment if having the expressway in HOV track and other non-HOV track.

Routine proceeds to step 515, is each (if there is not the separation of the data sample of execution in step 510, then all data samples are regarded as a group) of one or more data sample groups, calculates the average traffic feature of all data samples.This average traffic feature can comprise, for example, and average velocity, and such as the corresponding statistical informations such as standard deviation with respect to intermediate value.Routine then proceeds to step 520, to these one or more data sample groups each, carry out continuously and remove one (leave-one-out) analysis so that select the specific target data sample that will temporarily be removed and determine average traffic feature for remaining traffic feature.Difference between average traffic feature that is used for the remaining data sample and the average traffic feature that is used for all data samples from step 515 is big more, and then removed target data sample is to reflect that the possibility of exceptional value of public characteristic of other remaining data sample is just big more.In step 525, routine is then carried out the exceptional value analysis of one or more addition type alternatively, thereby thereby the group of removing two or more target data samples is continuously estimated their joint effect, but also can not carry out so additional exceptional value analysis in certain embodiments.After step 522, routine proceeds to step 590 removing the data sample be identified as exceptional value in step 520 and/or 525, and stores remaining data sample for follow-up use.In other embodiments, routine alternatively can be transmitted to one or more client with remaining data sample and uses.Routine is followed step 595 to determine whether continuation.If continue, then routine turns back to step 505, if do not continue, then routine proceeds to step 599 and finishes.

Fig. 6 is the process flow diagram of the exemplary embodiment of data sample speed estimator routine 600.This routine can provide by for example carrying out the data sample velocity estimation assembly 356 of Fig. 3 and/or the data sample velocity estimation assembly 107 of Fig. 1, for example estimates the current average velocity of this road segment segment in the time period based on each data sample that is used for road segment segment.In this exemplary embodiment, routine is the continuous calculating of each execution road segment segment average velocity of a plurality of time intervals or time window in the time period, but to call alternatively can be (for example, the estimating a plurality of time intervals via a plurality of routine call) that is used for the single time interval to each of routine in other embodiments.For example, if the time period is 30 minutes, then can carry out new average velocity in per five minutes calculates, for example with time interval of 5 minutes (and therefore each time interval not overlapping) with the previous or follow-up time interval, or with time interval (so overlapping with the adjacent time interval) of 10 minutes.

This routine begins in step 605, receive indication, the data sample of its indication road segment segment in the time period (for example, from the next data sample of the data readings of mobile data source and physical sensors), or the insufficient data of indication road segment segment in the time period, but can only from mobile data source and sensing data reading, receive a data sample in certain embodiments.The data sample that is received can be, for example, obtains from the output of data sample exceptional value remover routine.Similarly, can obtain the indication of inadequate data from data sample exceptional value remover routine.In some cases, the indication of inadequate data can be based on having data sample in shortage, be wrong (for example, adjusting assembly 105) for example by the sensing data of Fig. 1 when in the time period, not coming data sample and/or losing or be detected as when some or all data readings of road segment segment from the mobile data source related with road segment segment.In this example, routine continues to determine whether to have received the not enough indication of data in step 610.If then routine proceeds to step 615, if not, then routine proceeds to step 625.

In step 615, routine is carried out the embodiment (describing with reference to Figure 14) of magnitude of traffic flow estimation device routine to obtain the average traffic speed of the estimation of road segment segment in the time period.In step 620, routine then provides the indication of the average velocity of estimation.In step 625, routine starts from first time interval and is that the average velocity of wanting estimated is selected the next time interval or time window.In step 630, routine then is the average traffic speed of data sample calculating weighting in this time interval, and based on one or more factors to the data sample weighting.For example, in an illustrated embodiment, to the weighting of each data sample based on stand-by period of data sample and (for example change, with linearity, index, or step-by-step movement mode), for example give near the bigger weight of the data sample of time interval end (because they more can be reflected in the actual average speed of time interval end).In addition, in an illustrated embodiment data sample can also be further based on the source of data and weighting, for example no matter lay particular stress on or on the low side, the data readings weighting that comes from physical sensors is different from the data sample weighting that comes from vehicle and other mobile data source.In addition, in other embodiments, in weighting, can use various other factorses, comprise based on each sample---for example, can be different from data readings weighting data readings weighting from another physical sensors from a physical sensors, thereby the available information that reflects relevant sensor (for example, in the physical sensors one be intermittent error or have more coarse data readings resolution than another sensor), and the data sample that comes from a vehicle or other mobile data source can be similarly based on the information in relevant mobile data source with the differently weighting of data sample that comes from another such vehicle or mobile data source.Other type of the factor that can use in weighting comprises the value of the confidence or other estimation of possible errors in the particular data sample in certain embodiments, particular data should with related degree such as particular lane highway section.

After step 630, routine proceeds to step 635 so that the indication of average computation traffic speed in the time interval to be provided, and for example stores this information and/or will offer client to information for follow-up use.In step 640, routine obtains inherent step 605 reception of time period information obtainable additional data sample afterwards subsequently alternatively.In step 645, then determine whether in the time period, will calculate more time at interval, and if like this, then routine turns back to step 625.If alternatively there is not more time at interval, or after step 620, then routine proceeds to step 695 to determine whether continuation.If continue, then routine turns back to step 605, and if not, then proceed to step 699 and end.

Fig. 7 is the process flow diagram of the exemplary embodiment of data sample flow estimation device routine 700.Routine can be passed through, for example, the embodiment of the data sample flow estimation assembly 108 of the data sample flow of execution graph 3 estimation assembly 358 and/or Fig. 1 and providing is so that estimation traffic traffic characteristic rather than the average velocity in particular lane highway section in special time period.In this exemplary embodiment, the vehicle total amount of wanting estimated traffic characteristic to be included in to arrive on the time period inherent particular lane highway section or exist (or other mobile data source) and in the time period percentage occupancy of road segment segment with the point of reflection road segment segment or zone by percentage of time that vehicle covered.

Routine receives indication therein in step 705 beginning, its instruction time section road segment segment data sample and in the time period average velocity of road segment segment, or not enough data of road segment segment in the time period.Data sample can from, for example, the output of data sample exceptional value remover routine obtains, and average velocity can from, for example the output of data sample speed estimator routine obtains.The indication of not enough data can from, for example the output of data sample exceptional value remover routine obtains.In some cases, the indication of not enough data can be based on having data sample in shortage, for example losing or be detected as when some or all sensing data readings that do not come data sample maybe ought be used for road segment segment from the mobile data source related with road segment segment in the time period is wrong (for example, adjusting assembly 105 by the sensing data of Fig. 1).Routine then continues in step 706 to determine whether to have received not enough data indication.If then routine proceeds to step 750, if not, then routine proceeds to step 710.

In step 750, routine is carried out the embodiment of magnitude of traffic flow estimation device routine (describing with reference to Figure 14) to obtain the total amount and the occupancy of the estimation of road segment segment in the time period.In step 755, routine then provides the indication of the total amount and the occupancy of estimation.

In step 710, routine determines to provide the vehicle number (or other mobile data source) of data sample, for example by each data sample is related with specific mobile data source.In step 720, routine then determines to provide the most possible arrival rate of road segment segment of the vehicle of this data sample based on determined vehicle number on probability top.In certain embodiments, probability determines also further to use the information about the prior probability of the prior probability of such vehicle fleet size and specific arrival rate.In step 730, the number percent that routine then for example accounts for vehicle fleet based on determined quantity of vehicle and the relevant vehicle that data sample is provided is inferred in the time period sum by all vehicles of road segment segment, and the further fiducial interval of the total amount of estimation deduction.In step 740, routine is then inferred the number percent occupancy of road segment segment in the time period based on the total amount of being inferred, average velocity and average vehicle length.Also can estimate similarly in other embodiments the magnitude of traffic flow feature of interested other type.In an illustrated embodiment, routine then proceeds to the indication of step 790 with the number percent occupancy of total amount that deduction is provided and deduction.Behind step 755 or 790, continue if in step 795, determine; Then routine turns back to step 705; If do not continue, then proceed to step 799 and end.

Figure 11 is the exemplary embodiment of sensing data error in reading detector routine 1100.Routine can by, for example, the sensing data of execution graph 3 is adjusted assembly 353 and/or Fig. 1 sensing data and is adjusted assembly 105 and provide, thereby determines the health of one or more traffic sensors.In this exemplary embodiment,, carry out this routine to determine the health of one or more traffic sensors each time of one day based on the traffic sensor reading that in the indicated time period, obtains recently.In addition, in various embodiments, the traffic that is used for one or more each types is measured and can be by this routine analyses by the data of traffic sensor output, for example traffic speed, quantity, occupancy etc.And, some data that are used for traffic at least can be measured and/or collect in every way, for example (for example with various intervals level, the 5mph bucket that is used for the data set of velocity information), and in certain embodiments this routine can be analyzed data for specific traffic sensor with the interval level of one or more each that are used for that one or more traffics measure each (or other combined horizontal).

This routine begins in step 1105, and receive one or more traffic sensors and selected time classification (time classification recently for example, if routine is carried out to provide the result to be bordering on real-time mode after each time classification, or one or more previous time classifications of selecting for analysis) indication, but alternatively can be instructed to a plurality of chronological classifications in other embodiments.In certain embodiments, time can each all comprise time point classification (for example, 12:00AM-5:29AM and 7:30PM-11:59PM, 5:30AM-8:59AM by it, 9:00AM-12:29PM, 12:30PM-3:59PM, 4:00PM-7:29PM, and 12:00AM-11:59PM) and/or date category is (for example, Monday is to Thursday, Friday, Saturday and Sunday, or alternatively have Saturday and Sunday together in groups) the time classification and modeling.In each embodiment, can select specific chronological classification in every way, comprise and (for example be reflected in time period that traffic during it expects to have similar characteristics, based on call duration time and pattern, or the consistent behavior of other reflection traffic), if for example traffic is relative with early morning between the lights rare, then they are formed one group together.In addition, in certain embodiments, can determine to have the time period of similar magnitude of traffic flow feature by the analysis of history data, thereby no matter (for example distinguish different traffic sensors with artificial still automatic mode select time classification, by geographic area, road, single-sensor etc.).

Step 1110 to 1150 in, routine is then carried out circulation, and its analyzes each traffic sensor data readings of coming from indicated one or more traffic sensors of being used for indicated time classification to determine the traffic sensor health status of each traffic sensor during this time classification therein.In step 1110, from first traffic sensor, routine is selected the next traffic sensor in indicated one or more traffic sensors, and select indicated time classification (or, if alternatively indicated a plurality of time classifications, then be the next one combination of traffic sensor and indicated time classification) in step 1105.In step 1115, routine is retrieved the averaged historical data readings that is used for traffic sensor and is distributed in selected time classification.In certain embodiments, the historical data reading distributes can be based on the data readings that is provided in selected time classification by traffic sensor (for example, having striden across between the 4:00PM and 7:29PM on the date that time period such as the prolongation in nearest 120 days or recent 120 day cycle etc. comprised Monday to Thursday).

In step 1120, routine continues as selected traffic sensor and selected time classification is determined target traffic sensor DATA DISTRIBUTION.In step 1125, routine is then determined the similarity that target traffic sensor data readings distributes and historical traffic sensor data readings distributes.As the other places more detailed description, in certain embodiments, can determine by the Kullback-Leibler divergence of calculating between distribution of target traffic sensor data readings and the distribution of historical traffic sensor data readings in the tolerance of such similarity.In step 1130, as more going through ground elsewhere, routine is then determined the information entropy that target traffic sensor data readings distributes.

In step 1135, routine is that selected time classification estimates that the health of selected traffic sensor is (for example to carry out healthy classification by using various information then, indication " health " or " unhealthy ", or the value on " health " yardstick, for example from 1 to 100), it (for example comprises determined similarity, determined entropy and selected time classification in this embodiment, the selected hour is classification constantly, for example 4:00PM is to 7:29PM, and/or selected date category, such as Monday to Thursday).In other embodiments, can use the out of Memory type, for example want the indication 5mph bucket of the data set of velocity information (for example, for) of the interval degree of measured data.In one embodiment, can use neural network to classify, and in other embodiments, can use other sorting technique, comprise decision tree, Bayes classifier etc.

In step 1140, routine is that selected traffic sensor and selected time classification determined traffic sensor health status (in this example for healthy or unhealthy) based on the traffic sensor health and/or the other factors of estimation then.In certain embodiments, the health that no matter when is used for the traffic sensor of selected time classification is estimated as health in step 1135, and it is healthy that the health status that then is used for traffic sensor can be considered to.In addition, the health that no matter when is used for the traffic sensor of selected time classification be estimated as unhealthy (for example, in step 1135), and selected time classification has the moment at the hour classification of enough big time period (for example at least 12 or 24 hours) of related covering, and the health status that then is used for traffic sensor can be thought unsound.And, in certain embodiments, can retrieve and use the relevant information (for example being used for one or more previous and/or follow-up time periods) that relates to the time classification, thus in the long time period (for example, one day) to the health classification of traffic sensor.Such logic has reduced the interim unusual travel pattern of accurately reporting based on sensor and the sensor health status has been carried out the wrong negative risk of determining (for example determining that the health status of traffic sensor is unsound when in fact traffic sensor is health).

For example, may produce the negative definite of mistake owing in data readings, changing the same day significantly because of external factor (for example, traffic hazard, weather accident etc.).For example, the traffic accident of special traffic sensor place or near generation it may cause traffic sensor to provide unusual and irregular data readings in the short relatively time period (for example, one to two hour).If the sensor health status is definite only based on the data readings that is mainly obtained in the caused distribution time by traffic hazard, what then just might lead to errors is negative definite.By based on determining the state of unsound sensor, can reduce so wrong negative definite risk from (for example, the 12 or 24 hours) data readings that is obtained of relatively large time period.On the other hand, in general possibility is very low for negative definite (for example the determining that the health status of traffic sensor is unsound when in fact traffic sensor is healthy) of mistake, because the traffic sensor of fault can not provide the data readings that is similar to historical data reading (for example, the general travel pattern of reflection).Similarly, can determine that suitably the health status of traffic sensor is healthy based on the relatively short time period.

Some embodiment can be by with the routine of time classification shown in repeatedly carry out every day of reflection short period section (for example, prolonged the chronological classification of the moment at the hour classification of first three hour earlier to have, routine of execution in per three hours) and with the time classification that reflects the whole previous date (for example carry out a routine at least in every day, to prolong the time classification of the previous 24 hours moment at hour classification, at the executive routine at midnight) and realize this different logic.

In addition, determining of sensor states can be based on other factors, for example whether be selected time classification obtain sufficient amount data readings (for example, because sensor is the report data reading off and on) and/or based on the indication (for example, traffic sensor is stuck) of the sensor states that provides by traffic sensor.

In step 1145, routine provides the health status of determined traffic sensor.In certain embodiments, can serve as reasons other assembly (for example, the sensor data collection assembly 110 of Fig. 1) follow-up use and (for example store the traffic sensor health status, be stored in database or the file system) and/or it is directly offered other assembly (for example, data sample exceptional value remove assembly).In step 1150, routine determines whether to exist more a plurality of sensors (or combination of traffic sensor and time classification) to handle.If then routine proceeds to step 1110 continuation, if not, then proceed to step 1155 to carry out other suitable action.This other action can comprise, for example, and for each each of one or more time classifications that is used for a plurality of traffic sensors come periodically (for example, once a day, inferior on every Mondays) repeated calculation historical data reading distribute (for example, at least 120 days).Distribute by periodicity repeated calculation historical data reading, in the face of the traffic that gradually changes, routine can continue to provide determining of accurate traffic sensor health status.After step 1155, routine proceeds to step 1199 and returns.

Figure 12 is the process flow diagram of the exemplary embodiment of data readings error recovery device routine 1200.This routine can be by for example, and the sensing data that the sensing data of execution graph 3 is adjusted assembly 353 and/or Fig. 1 is adjusted assembly 105 and provided, thereby be identified for the data readings after the correction of one or more traffic sensors related with road segment segment.Shown in exemplary embodiment in, this routine can periodically be carried out (for example per five minutes) be used for being identified as by sensing data error in reading corrector routine unsound traffic sensor with correction data readings.In other embodiments, can carry out this routine as required,,, or alternatively in various environment, can not be used with the data readings after the correction that obtains to be used for the particular lane highway section for example by sensor data collection device routine.For example, in general all data samples by being identified for the particular lane highway section (for example, come from a plurality of data sources, the polytype that for example can comprise the mobile data source of traffic sensor and one or more dissimilar types) traffic flow conditions that whether provides enough data to analyze this road segment segment is carried out the analysis and the correction of data, if not, then do not carry out from the correction of the next data of each traffic sensor.

This routine begins in step 1205, wherein its receive the road segment segment related with one or more traffic sensors indication (for example, by come from sensing data error in reading detector routine, one or more associated traffic sensors have been classified as unsound result), and receive to want the indication of processed one or more time classifications (for example, wherein be classified as be unsound time classification at least potentially) alternatively at least one of the traffic sensor of association.In other embodiments, interested one or more traffic sensors can otherwise be indicated, for example by directly receiving the indication of one or more traffic sensors.In step 1210 to 1235, routine is carried out a circulation, wherein it handles unsound traffic sensor on indicated road segment segment, with the data readings after determining for these traffic sensors and correction is provided (for example in step 1205 indicated time classification) during one or more time classifications.

In step 1210, from first, routine is chosen in the next unsound traffic sensor in the indicated road segment segment.This routine also by select one or more during it traffic sensor before be designated as unsound time classification and wait and select the time classification that will use, for example one or more time classifications indicated in step 1205.In step 1215, that routine is determined whether to have other enough health in indicated road segment segment and can be used to the traffic sensor that the auxiliary reading that is used for the unhealthy sensor of selected time classification is proofreaied and correct.Whether this is determined can be based on existing at least predetermined amount of data (for example in the indication road segment segment during selected time classification, at least two) and/or predetermined percentage is (for example, at least 30%) healthy traffic sensor, and it is also conceivable that the relative position (for example, near adjacent or traffic sensor can be better than the sensor away from unhealthy traffic sensor) of healthy traffic sensor in the road segment segment of indication.If in step 1215, determine to exist enough healthy traffic sensors, then routine proceeds to step 1220, here based on the correction data reading that is identified for unhealthy traffic sensor from the data readings of coming at other healthy traffic sensor of the road segment segment that is used for selected time classification.Can determine the correction data reading in every way, for example by calculating from the average of two or more data readings of the healthy traffic sensor acquisition the indication road segment segment of selected time classification.In certain embodiments, all healthy traffic sensors may be used to averaging, but can only use selected healthy traffic sensor in other embodiments.For example, if the predetermined percentage of the traffic sensor in indicated road segment segment (for example, at least 30%) during selected time classification, be healthy, then can use all healthy traffic sensors to come averaging, otherwise can only use nearest predetermined quantity (for example, at least two s') healthy traffic sensor.

There are not enough healthy traffic sensors if alternatively in step 1215, determine in the indication road segment segment that is used for classification seclected time, then routine proceeds to step 1225, here it attempt based on relate to this traffic sensor/or the out of Memory of road segment segment be identified for the correction data reading of unhealthy traffic sensor.For example, such information can comprise the predict traffic conditions information that is used for road segment segment and/or unhealthy traffic sensor, be used for the forecast traffic related information of road segment segment and/or unhealthy traffic sensor, and/or be used for the historical average traffic related information of road segment segment and/or unhealthy traffic sensor.Can carry out the relative reliability that various logic reflects various information types.For example, in certain embodiments, use predict traffic conditions information (for example) can have precedence over the forecast traffic related information, use the forecast traffic related information can have precedence over historical average traffic related information again as long as can obtain.The additional detail that relates to prediction and forecast future transportation traffic conditions can be submitted on March 3rd, 2006, and the U.S. Patent application No.11/367 that is entitled as " Dynamic Time Series Prediction Of Future Traffic Conditions ", obtain in 463, its full content is incorporated in this as a reference.In other embodiments, execution in step 1215 and 1225 is not carried out if the data readings for example in step 1220 is always proofreaied and correct based on the best data that obtain from other healthy traffic sensor during selected time classification and/or relevant time classification.For example, if the predetermined percentage at least of all healthy traffic sensors (for example in the indication road segment segment of selected time classification, at least 30%) be healthy, then proofread and correct can be based on these all traffic sensors for data readings, otherwise based on the healthy traffic sensor that closes on most in indicated during selected time classification and/or relevant time classification and/or the road segment segment of closing on.

Behind step 1220 or 1225, routine proceeds to step 1230 and provides determined traffic sensor data readings as the correction reading that is used for the traffic sensor during selected time classification.In certain embodiments, determined traffic sensor data readings can be stored (for example, being stored in database or the file system) for the follow-up use of other assembly (for example, the sensor data collection assembly 110 of Fig. 1).In step 1235, the traffic sensor that routine determines whether to want processed and the additional combinations of time classification.If have, then routine turns back to step 1210, if not then proceed to step 1299 and finish.

Figure 13 is the process flow diagram of the exemplary embodiment of sensing data reading gatherer routine 1300.This routine can be passed through, for example, the sensing data reading collection assembly 355 of execution graph 3 and/or Fig. 1 sensing data reading collection assembly 110 provide, and for example determine and are provided at special time classification or the traffic related information of a plurality of traffic sensors (for example related with the particular lane highway section a plurality of traffic sensors) in the section At All Other Times.Shown in exemplary embodiment in, this routine is that carry out in the particular lane highway section, but in other embodiments can be from a plurality of traffic sensor group acquisition of informations of other type.In addition, this routine can provide by other routine of the estimation of carrying out traffic related information (for example replenishes, data sample flow estimation device routine) traffic related information of the information that provides provides traffic related information thereby can not provide in other routine under the situation of accurate estimation (for example because data deficiencies).

This routine is in step 1305 beginning and receive one or more section and one or more time classifications or the indication of section At All Other Times.In step 1310, routine begins to select next bar road segment segment of one or more indicated road segment segment from first.In step 1315, some or all traffic sensor data readings that can get that the routine acquisition is gathered in the indicated time period by all traffic sensors related with this road.Such information for example can be adjusted sensing data adjustment assembly 353 acquisitions of assembly 105 and/or Fig. 3 from the sensing data of Fig. 1.Particularly, routine can obtain the traffic sensor data readings and/or obtain the traffic sensor data readings of proofreading and correct from being confirmed as unsound traffic sensor for being confirmed as healthy traffic sensor in some cases, and for example those sensing data error in reading corrector routines by Figure 12 provide or determine.

In step 1320, routine is then collected the data readings that is obtained in one or more modes, thereby determines to be used in the indicated time period average velocity, amount and/or the occupancy of road segment segment.Average velocity can be for example by determining through the data readings averaging of the car speed of one or more traffic sensors reflection.The volume of traffic can be determined according to reporting vehicle quantity data reading.For example, given report is activated from sensor and begins loop sensor by the vehicle cumulative amount of sensor, then the volume of traffic two data readings that can obtain by deducting in the indicated time period and removed the result and inferred simply by the time interval between data readings.In addition, density can be determined based on determined average velocity, amount and average vehicle length, as describing in more detail elsewhere.In some cases, data readings weighting in every way (for example, passing through the age) is so that near more data readings has the influence bigger than old more data readings in average discharge is determined.

In step 1325, routine determines whether that then many road segment segment (or other group of a plurality of traffic sensors) will handle.If have, then routine turns back to step 1310, otherwise proceeds to step 1330 so that determined traffic flow information to be provided.In certain embodiments, can store determined flow information (for example, being stored in database or the file system) so that the RT information providing system 363 of the traffic data client 109 of the follow-up Fig. 1 of offering and/or Fig. 3.Next, routine proceeds to step 1339 and returns.

Figure 14 is the process flow diagram of the exemplary embodiment of magnitude of traffic flow estimation device routine 1400.This routine can provide by for example carrying out magnitude of traffic flow estimation assembly (not shown), thereby estimation in every way is used for various types of traffic flow informations of road segment segment.In this exemplary embodiment, for example can not obtain in enough data conditions for accurately carrying out their estimations separately when these routines, routine can be estimated the estimation of device routine call with acquisition amount and/or occupancy with the estimation of acquisition average velocity and/or by the data sample flow of Fig. 7 by the data sample speed estimator routine call of Fig. 6.

This routine in step 1405 beginning and reception channel highway section, one or more time classification or the indication of section and one or more traffic flow informations such as one or more types such as speed, amount, density, occupancies At All Other Times.In step 1410, routine determines whether to estimate based on one or more relevant road segment segment the traffic flow information of indication type, for example, the precise information that whether has the traffic flow information that is used for one or more types based on such road segment segment in the time period shown in one or more.Relevant road segment segment can be discerned in every way.For example, in some cases, the information of relevant road segment segment can comprise the relevant information that concerns between road segment segment, for example first road segment segment has usually and (for example is similar to second, adjacent) the travel pattern of road segment segment, thereby the traffic flow information that is used for second road segment segment can be used for estimating the magnitude of traffic flow on first road segment segment.In some cases, no matter analysis is to carry out in advance and/or dynamically, can determine such relation automatically, for example based on the section of two road separately magnitude of traffic flow pattern statistical study (for example, be similar to distributing in the different time similar data of previous discussion, but alternatively analyze in two or more different sensors such as the similarity between the same time) about discerning given traffic sensor.Alternatively, can select one or more adjacent road segment segment to come related indicated road segment segment and need not that any of particular kind of relationship determines between the road segment segment of having carried out.If determine based on relevant road segment segment estimation traffic flow information, then routine proceeds to step 1415 and is used for the value of the traffic flow information of indicated type based on the same type traffic flow information that is used for one or more relevant road segment segment estimation.For example, determine the average velocity (for example, by using the traffic speed that comes from an adjacent road section, or traffic speed averaging) of this road segment segment to coming from two or more adjacent road sections based on the average traffic speed of one or more adjacent road sections.

If alternatively in step 1410, determine not to be used for the traffic flow information of indicated road segment segment based on relevant road segment segment estimation, then routine proceed to step 1420 and determine whether in one or more indicated time periods based on be used for this indication road segment segment and instruction time section information of forecasting be that indicated road segment segment is estimated traffic flow information.In certain embodiments, such information of forecasting may only obtain under specific situation, obtains accurate current data simultaneously if for example repeat prediction (for example at ensuing 3 hours per 15 minutes once) at a plurality of following moment.Similarly, if the accurate input data that (for example, above three hours) is used to produce prediction in time expand are available, then can need not to obtain prediction by the employed future transportation condition information of this routine.Alternatively, in certain embodiments, the future transportation condition information of such prediction is owing to some other former thereby non-availability, for example owing to do not use in this embodiment.If in step 1420, determine based on information of forecasting estimation traffic flow information, then routine proceeds to step 1425, and the information of forecasting that system 360 obtains is provided and is the indication type of the time period estimation traffic flow information of the road segment segment of indication and indication based on the information of forecasting from for example Fig. 3.The additional detail that relates to prediction and forecast future transportation traffic conditions is the U.S. Patent application No.11/367 that is entitled as " Dynamic TimeSeries Prediction Of Traffic Conditions " that on March 3rd, 2006 submitted to, can obtain in 463, its full content is incorporated in this as a reference.

If alternatively determine in step 1420 is not that indicated road segment segment (is for example estimated traffic flow information based on information of forecasting, because this information can not get), then routine proceeds to step 1430 and determines whether and is indicated road segment segment estimation traffic flow information based on the forecast information that is used for this road segment segment and time period in the time period of one or more indications.In certain embodiments, can be for exceeding the following Time Forecast traffic of energy predict traffic conditions, for example in the mode of not using at least some the present situation information.Similarly, if can not obtain information of forecasting (for example, having surpassed three hours with regard to non-availability), then still can use forecast information, for example the information that obviously produces in advance owing to be used to produce the accurate input data of prediction.If determine in step 1430 based on forecast information estimation traffic flow information, then routine proceeds to step 1435 and is the traffic flow information of indicated road segment segment and time period estimation indication type based on the forecast information that provides system 360 to obtain from for example predicted traffic information.

If alternatively be not that indicated road segment segment (is for example estimated traffic flow information in step 1430 based on forecast information, because this information non-availability), then routine proceed to step 1440 and based on the historical average discharge information that is used for indicated road segment segment be indicated road segment segment and time period estimation indication type traffic flow information (for example, for the identical or corresponding time period, for example based on the time classification that comprises the moment at hour classification and/or date category).For example, if forecast information be unavailable (for example, because the input data of the time longer than the cycle that produces nearest prediction and forecast are unavailable, therefore can not produce new prediction can not produce new forecast), then routine can be used the historical average discharge information that is used for indicated road segment segment.Relate in the U.S. Patent application (application attorney docket is 480234.410P1) that is entitled as " Generating Representative Road Traffic FlowInformation From Historical Data " that the additional detail that produces historical average discharge information can submit at the same time and obtaining, its full content is incorporated in this as a reference.

After step 1415,1425,1435 or 1440, routine proceeds to step 1445 and the estimation traffic flow information of indicated type was provided for indicated road segment segment and indicated time period.The information that is provided can for example be returned to the routine of calling this routine (for example, data sample flow estimation device routine) and/or be stored (for example, being stored in database or the file system) for follow-up use.After step 1445, routine proceeds to step 1499 and returns.

Fig. 9 A-9C illustrate obtain with relevant road traffic condition information is provided in the action example in mobile data source.The information of relevant road traffic condition can be in every way obtains from the mobile device equipment or the subscriber equipment of vehicle (no matter based on), for example by (for example using Radio Link, satellite uplink, cellular network, WI-FI, packet radio etc.) transmission and/or when equipment reaches suitable butt joint (docking) or other tie point physics download (for example, in case the main base of return or other purpose with the suitable equipment that can carry out download of information just from fleet's download message).Though the information of the relevant road traffic condition of the very first time that obtains in second time that obviously was later than (for example provides various benefits, revise the prediction of the very first time, for the data of using institute's observed case have subsequently been improved prediction processing etc.), it for example can be the situation of slave unit physics download message, when with in real time or when being bordering on real-time mode and obtaining, such road traffic condition information provides additional benefits.Therefore, in at least some embodiment, mobile device with wireless communication ability can provide the information of at least some required relevant road traffic conditions continually, for example periodically (for example, per 30 minutes, 1 minute, 5 minutes etc.) but and/or when information needed time spent that can q.s (for example, for each data point relevant with road traffic condition information; For every N such data, for example wherein N is configurable number; Reach specific memory and/or transmission size etc. when fetched data).In certain embodiments, this frequent radio communication of the road traffic condition information of being obtained can also (for example replenished by the additional road traffic condition information that obtains At All Other Times, the continuous physical of slave unit is downloaded, via few frequency (less-frequency) radio communication that comprises the greater amount data), for example comprise additional data, comprise the acquisition of information of relevant a plurality of data points etc. corresponding to each data point.

Though by providing various benefits from mobile device with the road traffic condition information that real-time or other frequent mode obtain to be obtained, the radio communication of so in certain embodiments road traffic condition information that obtains can retrain in every way.For example, in some cases, the cost structure of transmitting data via specific radio link (for example, satellite is uploaded) from mobile device with few interval frequently (for example can be, per 15 minutes) transmission that takes place, perhaps mobile device can be programmed with such interval transmission in advance.In some other situation, mobile device may temporarily be lost the ability by the transmission of radio links data, for example owing to (for example lack wireless coverage in the zone at mobile device place, because the cellular radio receiver base station of not closing on), because other action of carrying out by the user of mobile device or equipment, or because the temporary transient problem of mobile device or associated transmitter.

Therefore, at least some such mobile devices can be assigned or be configured to store a plurality of data samples (or making so a plurality of data samples be stored in other associate device) in certain embodiments, can be transmitted together in a wireless transmission for use at least some information of a plurality of data samples.For example, at least some mobile devices are configured to can not (for example pass through the transmission of radio links data at mobile device in certain embodiments, mobile device transmits each data sample usually separately, for example per 30 seconds or 1 minute) time the cycle memory storage road traffic condition information data sample that obtains, and then these data samples of storing are transmitted together at the time durations of the next wireless transmission of appearance.Some mobile devices can also be configured to performance period property (for example per 15 minutes, or when the data of specified amount can be used for transmitting) wireless transmission, and at least some embodiment, can also be configured to during the time interval between the wireless transmission to obtain and a plurality of data samples of storage road traffic related information (for example with predetermined sampling rate, for example 30 seconds or one minute), and then during next wireless transmission, these data samples of storing are transmitted (or the subclass of these samples and/or set) together similarly.As an embodiment, if nearly the wireless transmission cost Shi $0.25 of 1000 information units and the size of each data sample are 50 units, then per minute sampling and transmission in per 20 minutes comprise that the data set (rather than per minute sends each sample individually) of 20 samples is very helpful.In such embodiments, though data sample possibility slight delay is (in the example of cyclical transmission, postponed between the transmission time period average half, suppose regular acquisition data sample), then the road traffic condition information that obtains from transmission still provides the real-time information that is bordering on.And, can produce and provide additional information based on a plurality of data samples of storing by mobile device in certain embodiments.For example, if specific mobile device only can obtain the information of relevant current present position during each data sample, but can not obtain the additional correlation information such as speed and/or direction, then such additional correlation information can be calculated based on a plurality of follow-up data samples or be determined.

Particularly, Fig. 9 A has described and has had several interconnective roads 925,930,935 and 940 and the exemplary area 955 (road 925 and 935 north-souths are walked, and road 930 and 940 East and West directions are walked) of the legend indication 950 of indication road north orientation direction.Though only shown the road of limited quantity, they can represent vast geographic area, for example across several miles interconnective expressways, or have striden the subclass of the avenue in several districts.In this example, the mobile data source (for example, vehicle, not shown) in 30 minutes cycle, travel to 945c from position 945a, and be configured to obtain and transmitted in per 15 minutes the data sample of expression current traffic condition.Therefore, when the mobile data source began to travel, it was in position 945a acquisition and transmit first data sample (as use asterisk in this example

Shown in), obtain and transmit second data sample at position 945b after 15 minutes, and obtaining and transmit the 3rd data sample at position 945c after 30 minutes altogether.In this example, the indication that each data sample comprises current location (for example, in gps coordinate), current direction (for example, north orientation), present speed (for example, 30 minutes are per hour) and the current time, as use the transmission of 945a of data value Pa, Da, Sa and Ta represented, and also can comprise out of Memory (for example, the identifier in indication mobile data source) alternatively.Acquisition although it is so and the current traffic condition information that provides provide more benefit, but can not determine a plurality of details from such data, comprise that whether route from position 945b to 945c is partly along road 930 or 940.And such sample data does not allow, for example will be in the part of the road between position 945a and the 945b 925 different road segment segment as the different traffic that can report and predict.

With with mode like Fig. 9 category-A, Fig. 9 B has described example 905, its in 30 minutes cycle the mobile data source from position 945a to 945c travelled interconnective road 925,930,935 and 940 and the per information (as represented) that sent relevant traffic in 15 minutes in mobile data source at the asterisk shown in position 945a, 945b and the 945c.But in this example, the mobile data source is configured to per minute and obtains and store data sample, and subsequent transmission is included in the preceding 15 minutes data from each data sample.Therefore, when travel between position 945a and 945b in the mobile data source, the mobile data source obtains the group 910b of 15 data sample 910b1 to 910b15, and in this example, and the arrow that utilizes time of data sample to sentence the direction indication in mobile data source is indicated each data sample.In this example, each data sample comprises current location, current direction, present speed and the indication of current time similarly, and comprises each these data values that are used for data sample 910b in the continuous transmission of position 945b.Similarly, travel between position 945b and 945c as the mobile data source, then the mobile data source obtains 15 data sample 910c1-910c15, and comprises each the fetched data value that is used for 15 data samples in the subsequent transmission of position 945c.By such additional data sample is provided, can obtain various additional information.For example, be easy to now determine that from the route of position 945b to 945c be partly along road 930 rather than road 940, and allow corresponding traffic related information is used for road 930.In addition, the data sample that specific data sample is adjacent with them can provide the various information of relevant road smaller portions, for example allow the road 925 between position 945a and 945b to be expressed as for example (for example reaching 15 different road segment segment, by each data sample is related with different road segment segment), its each all have the different road traffic condition of possibility.For example, can observe out intuitively, the average velocity that is used for data sample 910b1-910b6 is roughly static (because approximate equality ground interval data sample), and the average velocity that is used for data sample 9101-910b8 increases (because data sample is corresponding to each position that gradually is far apart out, reflected that the distance of travelling in the interval at given 1 minute becomes big between the data sample of this example of user), and the average velocity of data sample 910b1-910b15 descends.Though the data sample in this example directly provides the information of relevant such speed, data message such among other embodiment can obtain from the data sample information that only comprises current location.

Fig. 9 C has described the 3rd example 990, wherein the mobile data source in 30 minutes cycle from position 965a to the 965c interconnective road part of travelling, and the per information of transmitting relevant traffic in 15 minutes in mobile data source (as among position 965a, 965b and the 965c shown in the asterisk).As shown in Fig. 9 C, the mobile data source is configured to that per minute obtains and the storage data sample in this example, and subsequent transmission comprises in preceding 15 minutes the data from each of at least some data samples.Therefore, travel between position 965a and 965b as the mobile data source, then the mobile data source obtains the group 960b of 15 data sample 960b1-960b15.But, as the data sample 960b5-b13 by co (owing to do not detect mobile at these data samples, therefore employed in this example is annular rather than arrow, but for the sake of clarity with its independent demonstration rather than in the top of one another), about 9 minutes (for example, stopping at cafe) stopped in a side of road 925 in the mobile data source in this embodiment.Therefore, when when position 965b produces next transmission, transmission in certain embodiments can comprise all information that are used for all data samples, or alternatively (for example can omit at least some information, the information of omitted data sample 960b6-960b12, this is that then they do not provide additional useful information in this situation because if knowing the mobile data source does not still move between data sample 960b5 and 960b13).And, though do not illustrate here, but can omit the information of one or more such data samples in other embodiments, and can to postpone follow-up transmission all be available (for example, if based on data volume that will be sent out rather than the property transmission of performance period time) up to 15 data samples that will be transmitted.And, travel between position 965b and 965c as the mobile data source, then the mobile data source is obtaining data sample 960c13 and 960c14 (as in this embodiment with opening shown in circle rather than the arrow) in the current disabled zone of radio communication.In other embodiments, wherein each data sample all is independent transmission when obtaining but when not storing, and these data samples can be lost, but in this example, is storage and transmits with other data sample 960c1 to 960c12 at position 965c on the contrary.Though do not illustrate here, but the mobile data source (for example can also temporarily lose ability that the basic device that uses data to obtain obtains one or more data samples in some cases, if the mobile data source loses the ability a few minutes of obtaining the GPS reading) if---like this, then the mobile data source can be reported the data sample that other obtains and (for example be need not further reaction in certain embodiments, then allow the take over party to insert if desired or estimate these data samples), though can attempt in other embodiments (for example otherwise to obtain data sample, determine the position by using accurate inadequately mechanism, for example the cellular mobile telephone tower triangular is measured, or by estimating current location based on the position of previously known and follow-up average velocity and orientation, for example pass through dead reckoning), even if these data samples (for example have lower accuracy or degree of accuracy, can be by comprising degree to the low credibility of these data samples or higher possible errors, or by comprising how indication these and/or other data sample produces).

Though in each of Fig. 9 B and 9C, the example data sample only illustrates a vehicle or other mobile data source for brevity, but in other embodiments, can not use a plurality of data samples that are used for specific mobile data source to determine the particular course of being gathered by this mobile data source, and more specifically, even can be not other is related with each (for example, if the source of each mobile data sample is anonymous, or having nothing different with other source).For example, if a plurality of data samples that come from specific mobile data source and can't help the take over party be used in produce relate to these data samples collective data (for example, produce speed and/or directional information based on the continuous data sample that positional information only is provided), for example when such collection data comprise each data sample or are not used, can not provide such take over party to discern in certain embodiments and relate to mobile data sample source and/or indicate a plurality of data samples from identical mobile data source (for example, increasing the privacy that relates to the mobile data source) based on design decision.

Alternatively, in at least some such embodiment, a plurality of mobile datas source is used together to determine interested road condition information, for example uses a plurality of data samples that come from all mobile data sources to determine the acquisition of information of this road segment segment at particular lane highway section (or other parts of road).Like this, for example, the interested time period (for example, 1 minute, 5 minutes, 15 minutes etc.) in, each of a plurality of incoherent mobile datas source can provide one or more its data samples that oneself travel on the particular lane highway section that relate in this time period, if and each such data sample comprises speed and directional information (for example), then can determine average gathering speed, for example to be similar to mode for the road traffic sensors of a plurality of vehicle acquisition of informations through sensors for this time period and the road segment segment that usually moves in the same direction that is used for all data sources.Specific data sample can be related with the particular lane highway section in every way, for example by the data sample position is related with the road with proximal most position (or road segment segment) (no matter for any road, or only to satisfying the road of specific criteria, the category of roads that for example belongs to one or more indicated functions) and then select suitable road segment segment for this road, or the indication that provides with the data sample of associated road (or road segment segment) by the mobile data source by use.In addition, in at least some embodiment, for the purpose of assigning data sample to road and other purpose (for example, with north orientation track, expressway as the different track different with the south orientation track of expressway), road that will be except one-way road is as different road, if and like this, the direction that then is used for the mobile data sample can also be used to determine the suitable road related with data sample---but, in other embodiments, otherwise modeling, for example as a road (for example with two-way avenue, according to the average traffic of reporting and predicting for the vehicle that on both direction, moves), with each track of the expressway of multilane or other road as different logic road etc.

In certain embodiments, determine interested road condition information for the ease of using a plurality of mobile datas source, fleet can be configured to provide employed road sample in every way.For example, if identical starting point is all left in the similar time of every day by each large-scale fleet, then each vehicle can be configured to relate to how soon and how long to begin to provide data sample by difference, for example minimizes to be near the mass data all single starting point and/or to be provided to obtain and the variation during the transmission data sample.More specifically, the mobile data source device can be configured to carry out how and when obtaining data sample in every way, comprise the total distance that begins to cover based on from the starting point starting point of fleet's group (for example for), obtain and/or transmit the distance that begins to cover from last data sample, the T.T. of (for example vehicle is from time that starting point is left) experience from the outset, obtain and/or transmit the time of experience from last data sample, produce the indexical relation of relevant one or more indicated positions (for example, by, arrive, leave etc.) etc.Similarly, the mobile data source device can be configured to carry out how and when transmitting or providing one or more data samples that obtain in every way, for example when the statistics predetermined condition, comprise based on total distance from the starting point covering, obtain and/or transmit the distance of covering from last data sample, play the T.T. of experience from the outset, obtain and/or transmit the time of experience from last data sample, produce the indexical relation of relevant one or more indicated positions, the indication number of a plurality of data samples of having collected, the indicated data volume of having collected (for example, fill up or be filled in fact on the mobile device quantity of the buffer of storage data sample, or for example fill up or fill up in fact be used to transmit instruction time amount quantity) etc.

Fig. 8 is the process flow diagram that mobile data source information provides the exemplary embodiment of routine 800, for example can by operation be used for Fig. 3 one or more based on vehicle data source 384 and/or each the mobile data source device of other data source 388 (for example, subscriber equipment) and/or Fig. 1 based on other data source 102 of the data source 101 of vehicle and/or Fig. 1 provide.In this example, this routine is that specific mobile data source acquisition data sample is indicated current traffic, and suitably stores data sample so that subsequent transmission can comprise the information that is used for a plurality of data sources.

This routine begins in step 805, wherein retrieval will be used in as data sample and obtain and the parameter of the part that provides, and for example configuration parameter is used to indicate and when should obtains data sample and when should produce transmission of Information corresponding to one or more data samples.Routine proceeds to step 810 and waits for, up to obtaining data sample in time, for example (for example based on the parameter of being retrieved and/or out of Memory, passed through the indicated time quantum that the past data sample obtains, the past data sample that travelled obtain shown in distance, the indication obtain data sample etc. in continuous in fact mode).Routine then proceeds to step 815 with the mobile data sample that obtains based on current location and mobile data source, and stores data sample in step 820.If in step 825, determine also not arrive the time of transmission data, for example (for example measure the instruction time of the previous transmission of process based on parameter of being retrieved and/or out of Memory, the indication distance of having travelled and before having transmitted, indication is as long as it is available or transmit data sample etc. in continuous in fact mode), then routine is returned step 810.

Otherwise routine proceeds to step 830 with retrieval and select any data sample of being stored of (or from, from transmitting for the first time) because previous transmission.Routine then alternatively in step 835 based on a plurality of selected data samples (for example, the whole average velocitys that are used for all data samples, if the information of being obtained only provides positional information, then for being used for the average velocity of each data sample and direction etc.) produce collected data.But also can not carry out in other embodiments, the generation of the data of such collection.In step 840, routine then from selected data sample group, remove alternatively be used for some or all data samples some or all institute's information that obtain (for example, only transmission is used for the selected type of each data sample, remove those and exceptional value or wrong data sample occur, remove those data samples that do not move etc.) corresponding to the reality in mobile data source, in other embodiments, also can not carry out such information removes.In step 845, routine then is transmitted in the current information in current group of the data sample and the information of any collection that will use by rights to the take over party.In step 895, routine determines whether to continue (for example whether the mobile data source is continued to use and be movably), and if then turn back to step 810.Otherwise routine proceeds to step 899 and finishes.Can not transmit among the embodiment and situation of data in the mobile data source, no matter whether because temporary transient situation has still alternatively reflected the configuration at first of mobile data source, step 830-845 can not be performed and can transmit data up to the mobile data source or provide (for example, downloading via physics) because some or all of the data sample of previous transmission and obtained and storage.

As the previous ground of noticing, in case and the information that has obtained relevant road traffic condition, for example from one or more mobile datas source and/or one or more other source, then can use road traffic condition information in every way, for example report current road traffic condition, or use past and current next each prediction future transportation situation of road traffic condition information in a plurality of following times in real-time substantially mode.In certain embodiments, the type that is used to produce the input data of future transportation condition predicting can comprise the following situation of various current, past and expection, and can comprise at the fixed time at interval (for example from the output that prediction processing is come, three hours, or one day) in a plurality of following times each (for example, following per 5,15 or 60 minutes) prediction of the expection traffic that on each of institute interested a plurality of target track highway section, produced, as institute's description in more detail elsewhere.For example, the type of input data can comprise following: relevant be used in the geographic area interested each target track highway section current and pass by the information of the volume of traffic, for example network of selected road in the geographic area; Information about current and recent traffic hazard; Information about current, recent and future trajectory engineering; About current, past with expect the information (for example, precipitation, temperature, wind direction, wind speed etc.) of external weather condition; The information of, past current about at least some and following incident of arranging (for example, the type of incident, the start and end time of time expection, and/or the place of time or other position etc., for example be used for all incidents, the incident of indication type, very great incident, for example have be expected on the indicated threshold value attending etc. of (for example attendants of 1000 or 5000 expections)); Information (for example, whether school gives a lesson and/or the position of one or more schools) with the arrangement of relevant school.In addition, though in certain embodiments, the a plurality of following time of prediction future transportation situation is every point on time, but so in other embodiments prediction alternatively (for example can be represented a plurality of time points, time period), for example by being illustrated in the average of future transportation situation during these a plurality of time points or collecting tolerance.And some or all of input data can be known and represent (for example, the weather of expection) with changing definite degree, and can produce additional information and be illustrated in and be used for the prediction that produced and/or the credibility of other metadata.In addition, for a variety of causes and each time can initialization future transportation situation prediction, for example in periodic manner (for example, per 5 minutes), when receiving any or enough new input data, response is from the request that is used for coming etc.

Can use in certain embodiments some of same type of input data produce similarly the future transportation situation the longer-term limit forecast (for example, a following week, or following one month), but the input data of some types also can not be used in the forecast of such longer-term limit, for example relevant information at the present situation (for example, current traffic, weather or other situation) that forecasts the time that produces.In addition, the forecast of such longer-term limit can with the forecast of short-term limit relatively more the lowland frequency produce, and can be produced the forecast of comparison short-term limit more can reflect the different following time periods (for example, per hour rather than per 15 minutes).

Can also select to be used to produce the road and/or the road segment segment of future transportation condition predicting and/or forecast in every way.In certain embodiments, for a plurality of geographic areas (for example, the urban district) each produces future transportation condition predicting and/or forecast, wherein each geographic area has the road network of a plurality of interconnection---can select such geographic area in every way, be a prominent question for example based on current traffic condition information available easily the road traffic sensors network of at least some roads in this zone (for example, based on) and/or traffic congestion wherein.In some such embodiment, the road that is used to produce future transportation condition predicting and/or forecast comprises that those are easy to obtain the road of current traffic condition information, and in other embodiments, the selection of such road can be at least in part based on one or more other factorses (for example, size or capacity based on road for example comprise expressway and primary highway; Road traffic regulation based on the carrying traffic for example comprises the Class I highway and the blocked road that can mainly be substituted into such as the road of larger capacities such as expressway and primary highway; Based on the functional category of road, for example specified etc.) by federal expressway management board.In other embodiments, can be that a road produces future transportation condition predicting and/or forecast, and no matter its size and/or with the mutual relationship of other road.In addition, can select to be used to produce the road segment segment of future transportation condition predicting and/or forecast in every way, for example with each road traffic sensors as different section; For each road segment segment with a plurality of road traffic sensors composition group (for example, reduce producing the quantity of independent prediction and/or forecast) of putting together for example by composition group that the road traffic sensors of specific quantity is put together; Select road segment segment so that the logic relevant portion of the road of reflection traffic identical or fully similar (for example, strong related); For example based on the traffic related information of (for example, from vehicle and/or the data that produce the user of travels down, as more detailed institute discussions elsewhere) from traffic sensor and/or other source; Deng.

In addition, in each embodiment, can use future transportation condition predicting and/or forecast information in a different manner, as more detailed discussion ground elsewhere, be included in each time in every way (for example, by giving cellular mobile telephone and/or other portable consumer device with information transmission; By giving user's display message, for example by Web browser and/or application program; By information being offered other tissue and/or the entity of at least some information being provided to the user, for example analyze and modification information after the third party that provides of execution information etc.) such information (is for example offered user and/or tissue, response request is by periodicity transmission information etc.).For example, in certain embodiments, use prediction and/or forecast information to determine the travel route and/or the time of suggestion, for example between starting position and final position the optimal route by road net and/or carry out shown in the optimal time that travels, and such determining is based upon each prediction and/or the forecast information of a plurality of following times of one or more roads and/or road segment segment.

In addition, various embodiment are mutual for user and other client provide various mechanism to come with one or more traffic information systems (for example, the data sample management system 350 of Fig. 3, RT information providing system 363, and/or information of forecasting provides system 360 etc.).For example, some embodiment can and receive the corresponding client that responds for the request of producing and (for example provide mutual control, client-side program provides mutual user interface, based on Web browser interface etc.), for example request relates to the information of current and/or predict traffic conditions and/or requirement analysis, selection, and/or the information that relates to travel route is provided.In addition, some embodiment provide API (" application programming interfaces "), and it allows the client computing system to carry out some or all requests able to programmely, for example by internet message agreement (for example, Web service) and/or other communication mechanism.

Those skilled in the art also can understand, and can be provided in mode alternatively by function that routine provided in certain embodiments as discussed abovely, for example can be divided in a plurality of routines or focuses on several routines.Similarly, the routine shown in can provide than described more function in certain embodiments, for example when the routine shown in other alternatively lacks respectively or comprises such function, or works as the function quantity optional time that is provided.In addition, though various operation can be as shown carried out with ad hoc fashion (for example serial or parallel) and/or particular order, it will be understood by those skilled in the art among other embodiment these operations and also can carry out with other order and mode.Those skilled in the art can also be understood that the data structure of above-mentioned discussion can make up by different way, for example the individual data segmentation of structures is concentrated in a data structure in a plurality of data structures or with a plurality of data structures.Similarly, the data structure shown in can be stored than described more or less information in certain embodiments, for example when the data structure shown in other alternatively lacks respectively or comprises such information, or when the quantity or the type optional time of institute's canned data.

Be understandable that from above-mentioned,, under the situation that does not deviate from the spirit and scope of the present invention, can carry out various modifications although described certain embodiments at this for the purpose of example.Therefore, the present invention is except that claims and all not limited this quotes element as proof.In addition, although particular aspects of the present invention is discussed with the form of given claim, inventor's imagination contains various aspects of the present invention with any available claim form.For example, though current only can being stated as in aspects more of the present invention is embedded in the computer-readable medium, similarly others also can comprise.

Claims (310)

1. a computer-executed method is used for based on the data sample that has reflected the travels down situation, and at determine the estimation mean velocity information at the vehicle of these travels down, described method comprises:

Receive the indication in one or more highway sections of one or more roads, each road segment segment has the data sample of a plurality of associations, and described each data sample has reflected the report speed of the vehicle on report time place road segment segment;

For at least one road segment segment each, automatically estimate the average traffic speed of the vehicle that travels on inherent road segment segment of certain time period as follows:

The group that occurs the related multidata sample of the road segment segment of report time in identification and described time period;

Based on one or more attributes of the degree of accuracy of the report speed that influences these data samples of these data samples, determine weight for described group data sample;

At least in part based on the weighted mean of the report speed of described group data sample, determine the average traffic speed of estimation of the vehicle that travels on the inherent road segment segment of described time period, use the described weighted mean of determined weight calculation; With

Use the average traffic speed of one or more estimations to assist travelling on one or more roads.

2. according to the method for claim 1, wherein, the indication of one or more road segment segment of described one or more roads of described reception comprises: the indication of a plurality of road segment segment of described one or more roads, wherein at each of a plurality of time periods, the automatic estimation of the average traffic speed of the vehicle that execution was travelled on each of at least one road segment segment in the time period, wherein occur the group of the related multidata sample of at least one road segment segment of report time in identification and described time period, comprising:

In the time period, receive the information of the current traffic condition of relevant a plurality of road segment segment, the information that is received comprises a plurality of data samples of described time period, described each data sample all from one of a plurality of vehicles report and reflected that in described time period report time is in the report speed of a vehicle of the reported position of one of road segment segment, described a plurality of vehicle is the subclass of all vehicles of travelling on the inherent described road segment segment of described time period, the information of described reception also comprises a plurality of additional data samples in the described time period, described each additional data sample is all from the report of one of a plurality of traffic sensors of monitoring a plurality of road segment segment, and based on one one or one or more velocity readings of a plurality of vehicles at the position of the road segment segment of one or more report times in the described time period, reflection report speed; With

For a plurality of road segment segment each, discern the group of the multidata sample of described road segment segment in the described time period, described multidata sample is from a plurality of data samples and a plurality of additional data sample at least one,

The one or more attributes that wherein are used for data sample weight, this group of determining data sample comprise: the age of data sample report time and the source of data sample, so that the data sample remote more for report time gives littler weight than report time data sample more recently, and give different weights with the data sample of reporting from traffic sensor for the data sample that the vehicle from one or more roads is reported

Thereby based on having reflected the data sample of actual vehicle travel conditions on the road segment segment, determine the average traffic speed of road segment segment, described data sample is weighted with the age of reflection data sample and the source of data sample.

3. according to the method for claim 2, wherein, at least one at least one each of each and described road segment segment for the described time period, identification group at the multidata sample of described road segment segment in the described time period comprises: be positioned at the multidata sample of a plurality of data samples on the described road segment segment and the multidata sample that is positioned at a plurality of additional data samples on the described road segment segment from its position in the described time period from its reported position in the described time period.

4. according to the method for claim 2, wherein, at least one at least one each of each and road segment segment for the time period, each the weight of each data sample of determining this group is also at least in part based on the expection degree of accuracy at the report speed of data sample, so that for the data sample that its report speed has low expection degree of accuracy, the data sample that has the higher expected degree of accuracy than its report speed is given lower weight.

5. according to the method for claim 4, wherein, for at least one time period and at least one road segment segment, determine each weight of each data sample of this group by using the exponential weighting function, so that for the data sample that its report speed has low expection degree of accuracy, the data sample that has the higher expected degree of accuracy than its report speed is given the weight that index reduces.

6. according to the method for claim 2, also comprise, for each of at least one described time period and each of at least one described road segment segment, the described time period is divided into a plurality of overlapping time windows, and use the data sample of its report time in this time window, carry out the estimation of the average traffic speed of the vehicle that travels on the described road segment segment in the described time period at each time window, so that at least some data samples are used for a plurality of time windows and give different determined weights to these time windows.

7. according to the method for claim 2, wherein, for each of at least one described time period and each of at least one described road segment segment, the average traffic speed of estimation of determining the vehicle that travels on the inherent described road segment segment of described time period comprises: produce the value of the confidence at the average traffic speed of estimation, with the possible errors degree of the average traffic speed of reflection estimation, use the average traffic speed of one or more estimations in the described time period to comprise: to use the value of the confidence that is produced with auxiliary following travelling on described one or more roads.

8. according to the method for claim 2, wherein, use the data sample that receives recently to carry out estimation with real-time mode to the average traffic speed of the vehicle that travels on the inherent road segment segment of described time period, wherein carry out the use of the average traffic speed of one or more estimations in the described time period, to assist travelling of on described one or more roads, being about in real-time basically mode.

9. method according to Claim 8, wherein, use the average traffic speed of one or more estimations in the described time period to comprise to assist travelling on one or more roads: at least in part based on the volume of traffic of estimating that average traffic speed is inferred at least one road segment segment of one or more roads, and to consider that one or more people that will travel provide the information about the volume of traffic of average traffic speed of estimation and deduction on described one or more roads.

10. according to the process of claim 1 wherein, for one or more each of at least some road segment segment, the one or more attributes that are used for determining described group data sample of weight comprise the age of the report time of described data sample.

11. method according to claim 10, wherein, age based on the report time of described data sample is carried out the Weight Determination of data sample, so that give littler weight for report time data sample remote more than report time data sample more recently.

12. method according to claim 1, wherein, one or more each at least some described road segment segment, the data sample that provides from a plurality of sources is provided the data sample related with described road segment segment, and the one or more attributes of data sample that wherein are used for determining the group of weight comprise the source of data sample.

13. method according to claim 12, wherein, at least one each for described one or more road segment segment, one of a plurality of sources of data sample comprise one or more vehicles, described one or more vehicle travels on described road segment segment and has reported data sample based on travel conditions, another of a plurality of sources of data sample comprises one or more traffic sensors, described traffic sensor monitor road section and based on having reflected that the reading through vehicle comes the report data sample.

14. method according to claim 12, wherein, for at least one each of described one or more road segment segment, a plurality of sources of data sample comprise a plurality of vehicles, and described vehicle travels on road segment segment and all reports the position of one or more reflection vehicles and/or the data sample of speed.

15. method according to claim 12, wherein, at least one each for described one or more road segment segment, a plurality of sources of data sample comprise a plurality of traffic sensors, described traffic sensor monitor road section, and all report based on the one or more one or more data samples that pass through the reading of vehicles of reflection.

16. method according to claim 12, also comprise, at least one each for described one or more road segment segment, estimation is used for each the reliability of a plurality of sources of the data sample of road segment segment, wherein determine the weight of these data samples based on the source of data sample, so that for the data sample that its source has low estimation reliability, the data sample that has higher estimation reliability than its source gives lower weight.

17. according to the process of claim 1 wherein, for one or more each of at least some road segment segment, the one or more attributes of data sample that are used for determining the group of weight comprise the total amount of the data sample of this group.

18. method according to claim 1, wherein, one or more each at least some road segment segment, for the data sample of this group is carried out Weight Determination, so that for the data sample of the lower expection degree of accuracy of its attribute reflection, reflect that than its attribute the data sample of higher expected degree of accuracy gives lower weight.

19. according to the process of claim 1 wherein,, determine weight for the data sample of this group and used exponential weighting for one or more each of at least some road segment segment.

20. according to the process of claim 1 wherein,, also determine weight for the data sample of this group based on one or more the present situations for one or more each of at least some road segment segment.

21. method according to claim 20, wherein, described the present situation comprises at least one in the following content: traffic hazard recently, motion race, when which in the time in the previous day (time-of-day), all several (day-of-week) in the last week, which day (day-of-month) in current January, current January with work as last annual control some months (month-of-year) in the week (week-of-month).

22. method according to claim 1, wherein, one or more each at least some road segment segment, the data sample related with the road segment segment of the group of being discerned provides from one or more vehicles, described vehicle travelled on road segment segment and based on travel conditions report data sample in the described time period, described one or more vehicle is the subclass of all vehicles of travelling on road segment segment in the described time period, and determine the average traffic speed of estimation of the vehicle that on road segment segment, travels in the described time period, the average traffic speed of all vehicles that in the described time period, on road segment segment, travel with estimation.

23. according to the process of claim 1 wherein,,, carry out the estimation of the average traffic speed of the vehicle that in the time period, on road segment segment, travels for a plurality of different time periods each for one or more each of at least some road segment segment.

24. method according to claim 1, wherein, one or more each at least some road segment segment, at each of a plurality of time windows overlapping in the described time period, the estimation of the average traffic speed of the vehicle that execution was travelled on road segment segment in the time period is so that be used at least some of the associated data sample of road segment segment each of a plurality of time windows.

25., wherein,, be modified in interior a plurality of overlapping time windows of described time period and reflect one or more current situations for one or more road segment segment each according to the method for claim 24.

26. method according to claim 1, wherein, one or more each at least some road segment segment, the estimation of the average traffic speed of the vehicle that travels on the inherent road segment segment of described time period is comprised the value of the confidence that is identified for the average traffic speed estimated, the use of the average traffic speed of described one or more estimations comprises: use one or more determined the value of the confidence, assist travel the future on described one or more roads.

27. according to the process of claim 1 wherein, the use of the average traffic speed of one or more estimations comprises: infer the volume of traffic based on the average traffic speed of one or more estimations at least one described road segment segment at least in part.

28. method according to claim 1, wherein, the use of the average traffic speed of one or more estimations comprises: the indication of the average traffic speed of described one or more estimations being provided for one or more people, make decision when the described one or more travels down with auxiliary people.

29. method according to claim 28, wherein, be used for described established data sample with respect to reception, people carry out the use of determining of the average traffic speed of described one or more estimations and the average traffic speed of described one or more estimations in real-time basically mode, so that can determine basically in real time.

30., carry out at least one automatic estimation of the average traffic speed of described estimation in real-time basically mode according to the process of claim 1 wherein.

31. method according to claim 1, wherein, one or more each at least some road segment segment, have only when the identification group of the multidata sample related with described road segment segment exceeds threshold value even as big as the average weighted statistical efficiency of the speed that makes report, just determine in the described time period the average traffic speed of estimation based on the weighted mean of the report speed of described group data sample at least in part at the vehicle of described travels down.

32. a computer-readable medium, its content can make computing equipment come to estimate mean velocity information for the vehicle that travels that by carrying out following method described method comprises:

Receive the indication of multidata sample, each described data sample has reflected the speed at one of a plurality of vehicles of travels down;

Based on the speed of the weighting scheme pooled data sample that uses the weight related with data sample, estimate the average traffic speed at the vehicle of described travels down at least in part, associated weight comprises a plurality of different weights; With

Provide the indication of the average traffic speed of being estimated, so that travelling on the road.

33. computer-readable medium according to claim 32, wherein, each multidata sample has the report time with velocity correlation, the average traffic speed of being estimated is used in the time period vehicle in travels down, the described time period comprises report time, comprise in the estimation of the average traffic speed of the vehicle of travels down and to determine the weight related with each data sample, with the expection degree of accuracy of the speed that reflects these data samples, comprise to come the weighted mean of the speed of determining data sample based on the mode of determined weight with the speed of the mode pooled data sample of weighting.

34. computer-readable medium according to claim 33, wherein, by a plurality of vehicle report multidata samples in travels down, wherein said a plurality of vehicle is the subclass at all vehicles of travels down, determined weighted mean is represented the average traffic speed of described a plurality of vehicles, the average traffic speed representative of being estimated is at the average traffic speed of all vehicles of travels down, for ease of providing the indication of the average traffic speed of being estimated to comprise: present the average traffic speed of estimation to the driver of vehicle, so that in the decision of the relevant travels down of influence in the use of travels down.

35. according to the computer-readable medium of claim 32, wherein, computer-readable medium is the storer of computing equipment.

36. according to the computer-readable medium of claim 32, wherein, computer-readable medium is a data transmission media, transmits data-signal that produced, content.

37. according to the computer-readable medium of claim 32, wherein, described content is to make computing equipment carry out the instruction of described method when carrying out.

38. a computing system that is configured to driving vehicle estimation mean velocity information comprises:

First assembly is configured to each at a plurality of roads, receives the indication of a plurality of data samples related with road, and described each data sample all is reflected in the speed of the vehicle of travels down; With

Data sample velocity estimation assembly is configured to each at a plurality of roads:

Determine the weight of the data sample related with road based on one or more attributes of described these data samples;

Based on the weighted mean of the speed of the data sample related with road, the one or many estimation uses determined weight to calculate weighted mean at the traffic speed of the vehicle of travels down at least in part; With

Provide the indication of estimation traffic speed, to assist travelling on road.

39. computing system according to claim 38, wherein, at least one each for a plurality of roads, each of a plurality of data samples related with road all has the report time with velocity correlation, the estimation traffic speed is used at the vehicle that comprises in the time period of report time in travels down, determine the weight of the data sample related with road, to reflect the expection degree of accuracy of these data sample speed, and provide the indication of estimation traffic speed, so that assist travelling on road to comprise: the driver to vehicle presents the estimation traffic speed, to be used to influence relevant decision in travels down.

40. according to the computing system of claim 38, wherein, described first assembly and described data sample velocity estimation assembly include the software instruction for execution of the storer that is used for described computing system.

41. computing system according to claim 38, wherein, described first assembly comprises receiving trap, be used for each at a plurality of roads, receive the indication of the multidata sample related with road, each data sample has reflected the speed at the vehicle of travels down, described data sample velocity estimation assembly comprises device, be used for each at a plurality of roads, determine the weight of the data sample related with road based on one or more attributes of these data samples, come the traffic speed of the vehicle of one or many estimation travels down at least in part based on the weighted mean of the speed of the data sample related that uses determined weight calculation with road, and the indication of estimating traffic speed is provided, to assist travelling on road.

42. a computer-executed method is used for automatically providing data sample to be used to estimate the traffic on these roads from the vehicle of travels down, described method comprises:

At in each of a plurality of vehicles of one or more travels down, a plurality of continuous time of predetermined length section each in provide the information of closing the vehicle travel conditions,

Obtain the multidata sample from the geolocation device of travelling with vehicle in the time period, each data sample is that the different acquisition time place in the time period obtains, and is included in the report geographic position and the speed of described acquisition time place vehicle;

In the time period, temporarily in memory device, store a plurality of data samples; With

In the end of time period,

Send a plurality of data samples of storing together to long-range traffic information system in individual data transmission by wireless data transmitter, described long-range traffic information system is configured to use at least some data samples to assist travelling of other vehicles on described one or more roads; With

Remove a plurality of data samples of storing from memory device, so that can be at the temporary transient data sample that is obtained of storing of next time period.

43., wherein, for a plurality of vehicles each, carry out the providing of information of relevant vehicle ' by the one or more computing systems on vehicle according to the method for claim 42.

44. according to the method for claim 43, wherein, all described a plurality of vehicles send data sample to single long-range traffic information system, described method also comprises, under the control of described single long-range traffic information system:

Repeatedly receive data transmission from described a plurality of vehicles, each data transmission that is received comprises a plurality of data samples of vehicle, and described data sample is geographic position and speed that obtain and that reported these acquisition time place vehicles at different acquisition time place; With

For each of a plurality of road segment segment of one or more roads and each of a plurality of time periods,

Retrieval from acquisition time in the described time period and the data sample that receives corresponding to a plurality of vehicles of described road segment segment of its report geographic position;

At least in part based on the report speed of institute's data retrieved sample, at the estimation average lane travel conditions of all vehicles that in the described time period, on described road segment segment, travel; With

During the described time period or near moment place, use described estimation average lane travel conditions help on the described road segment segment or near the travelling of other vehicle.

45. method according to claim 44, wherein, use estimation average lane travel conditions to help travelling of other vehicle and comprise in the following content at least one: send the information of relevant described estimation average lane travel conditions for other vehicle in real-time basically mode, and the information that produces relevant future trajectory travel conditions, wherein predict described future trajectory travel conditions based on described estimation average lane travel conditions at least in part.

46. according to the method for claim 43, wherein, at least one of described a plurality of vehicles each, the geolocation device of vehicle is GPS (" the GPS ") receiver that is installed in the vehicle.

47. according to the method for claim 43, wherein, among at least one of described a plurality of vehicles each, the geolocation device of vehicle is by the entrained cellular mobile telephone of passenger in the vehicle.

48. method according to claim 43, wherein, for among at least one of described a plurality of vehicles each, each data sample of the vehicle that is obtained comprises: the indication of the vehicle-state of the indication of the equipment state of the geolocation device that the report orientation of the acquisition time place vehicle of data sample, the acquisition time place and the vehicle of data sample travel together, the acquisition time place vehicle of data sample and with described geolocation device and/or the related unique identifier of vehicle.

49. method according to claim 48, wherein, at least some each for a plurality of vehicles, the wireless data transmitter that is used to send a plurality of data samples is a satellite transmitter, it only can transmit limited amount data, for at least some each of a plurality of vehicles, in the individual data transmission, send a plurality of data samples by wireless data transmitter together and comprise: before the individual data transmission, handle a plurality of data samples, to reduce the data volume that will be included in the individual data transmission.

50. method according to claim 49, wherein, for a plurality of vehicles at least some one of, provide the information of a relevant traveling state of vehicle to comprise in a period of time: in a described time period, to obtain the one or more additional data samples that are used for a described vehicle, so that described one or more additional data sample be that a plurality of data samples of obtaining of a vehicle are different in a period of time, and handling a plurality of data samples before at the transmission of the individual data of a vehicle comprises: based on the additional data sample that is confirmed as reflecting the interested travel conditions of institute, determine to remove described additional data sample from this individual data transmits.

51. according to the method for claim 42, wherein, at least some each of a plurality of vehicles, the predetermined length of each time period is 15 minutes and obtains described data sample with about 1 minute time interval.

52. an equipment that is used for vehicle provides the method for relevant vehicle traveling information on one or more roads automatically, described method comprises: under the control of described equipment,

Obtain a plurality of data samples, obtain each described data sample, to be reported in the geographic position and the travelling characteristic of described acquisition time place vehicle at different acquisition time;

Store described a plurality of data sample, up to satisfying one or more predetermined conditions; With

After satisfying described one or more predetermined condition, automatically provide described a plurality of data sample to traffic information system in individual data transmission, described traffic information system is configured to use the data sample that provides from a plurality of vehicles to assist travelling on road.

53. according to the method for claim 52, wherein, described one or more predetermined conditions comprise the indicating length of wherein carrying out the time period of obtaining a plurality of data samples.

54. according to the method for claim 53, wherein, the indicating length of described time is at least 10 minutes, and the acquisition time of data sample appears in about per minute.

55. method according to claim 53, wherein, repeatedly carry out storage and these data samples providing in the individual data transmission of the obtaining of a plurality of data samples, these data samples, so that in the prolongation cycle of a plurality of time periods that comprise indicating length, provide the information of closing the vehicle travel conditions.

56. according to the method for claim 52, wherein, described one or more predetermined conditions comprise will carry out the instruction time that a plurality of data samples are provided in the individual data transmission.

57. according to the method for claim 52, wherein, described one or more predetermined conditions comprise the indication quantity of a plurality of time samples that will be acquired.

58. according to the method for claim 52, wherein, described one or more predetermined conditions comprise the designation data amount that will obtain from described a plurality of data samples.

59. according to the method for claim 52, wherein, described one or more predetermined conditions comprise the indication distance of vehicle '.

60., wherein, after obtaining the indicating length that the past data sample passed through the time, obtain at least some each of described a plurality of data samples according to the method for claim 52.

61., wherein, travelled after the indication distance, obtain at least some each of described a plurality of data samples from obtaining past data sample vehicle according to the method for claim 52.

62., wherein, after vehicle has arrived one or more indications geographic position, obtain at least some of described a plurality of data samples according to the method for claim 52.

63., wherein, repeat storage, these a plurality of data samples providing in the individual data transmission of the obtaining of a plurality of data samples, these a plurality of data samples according to the method for claim 52.

64. according to the method for claim 52, wherein, each of at least some data samples uses latitude and longitude coordinate to come the geographic position of reporting vehicle.

65. according to the method for claim 52, wherein, each of at least some data samples uses the position of the road that travels based on described vehicle to come the geographic position of reporting vehicle thereon.

66. according to the method for claim 52, wherein, the report travelling characteristic of the vehicle of each of described at least some data samples comprises the speed of vehicle.

67. according to the method for claim 52, wherein, the report travelling characteristic of the vehicle of each of described at least some data samples comprises the orientation of vehicle.

68. according to the method for claim 52, wherein, the report travelling characteristic of the vehicle of each of described at least some data samples comprises at least one in the state of the distance of vehicle ' and vehicle.

69. method according to claim 52, wherein, each of at least some data samples also comprises at least one of following content: be used to obtain the state indication geographic position, geolocation device of data sample, the indication of the identifier related with described geolocation device and/or vehicle.

70. according to the method for claim 52, wherein, the equipment in vehicle comprises GPS (" the GPS ") receiver that geographical location information is provided.

71. according to the method for claim 52, wherein, the equipment in vehicle is the entrained cellular mobile telephone of passenger in the vehicle.

72. according to the method for claim 71, wherein, the geographic position of each of described at least some data samples is based on one or more honeycomb mobile telephone network transmitters that can communicate with described cellular mobile telephone.

73. according to the method for claim 71, wherein, provide a plurality of data samples to comprise in the individual data transmission: the honeycomb mobile telephone network by described cellular mobile telephone sends the individual data transmission.

74. according to the method for claim 52, wherein, the equipment in vehicle is mounted in the computing equipment in vehicle, and/or as the computing equipment of the part of vehicle.

75. method according to claim 74, wherein, described computing equipment is the part of wireless network, the geographic position of each of described at least some data samples at least in part based on one or more wireless network access points of described computing device communication, in the individual data transmission, provide a plurality of data samples to comprise and send described individual data transmission by described wireless network.

76. according to the method for claim 52, wherein, the equipment in vehicle comprises satellite transmitter, provides a plurality of data samples to comprise by described satellite transmitter in the individual data transmission and sends described individual data transmission.

77. according to the method for claim 52, also being included in the individual data transmission provides before a plurality of data samples, revises the information related with described a plurality of data samples to reduce the data volume that will be included in the individual data transmission.

78. method according to claim 52, also being included in the individual data transmission provides before a plurality of data samples, acquisition includes one or more additional data samples of relevant vehicle traveling information, and determines to remove the additional data sample described individual data transmission from a plurality of data samples that provide.

79. according to the method for claim 78, wherein, determine to remove the additional data sample at least in part based on be confirmed as reflecting the additional data sample of interested travel conditions.

80. method according to claim 52, wherein, use comprises from the data sample that a plurality of vehicles provide: at least in part based on the geographic position of being reported with from the travel conditions of the data sample that is provided, the road driving situation of the vehicle that estimation is travelled at least a portion of one or more roads, and use the road driving situation estimated help described one or more roads to small part or near may the travelling of other vehicle.

81. 0 method according to Claim 8, wherein, the road driving situation of being estimated is included in the average velocity of all vehicles that travel at least a portion of described one or more roads.

82. 0 method according to Claim 8, wherein, use the road driving situation of being estimated to help at least one that other vehicle that travels comprises following content: the information that after estimation, produces the road driving situation of the estimation that can be used for other driving vehicle in real-time basically mode, with the information that produces about the future trajectory travel conditions, predict described future trajectory travel conditions based on the road driving situation of being estimated at least in part.

83. 0 method according to Claim 8, wherein, under the control of the traffic information system of distance vehicle remote, automatically perform the road driving situation estimation and estimate the use of road driving situation.

84. method according to claim 52, wherein, one or more predetermined conditions comprise the current ability of carrying out wireless data transmission, wherein during obtaining a plurality of data samples, occur temporarily can not carrying out wireless data transmission, described method also comprises: but obtain other data sample when the wireless data transmission time spent, and each described other data sample is offered traffic information system and need not to store described other data sample.

85. according to the method for claim 52, wherein, described one or more predetermined conditions comprise vehicle arrived can a fill order data transmission the position.

86. method according to claim 52, wherein, the ability of determining the vehicle geographic position is temporarily unavailable, when temporary transient at least one data sample that obtains when unavailable of the ability in the geographic position of determining vehicle each the geographic position to small part based on following content: but from the extrapolation and/or the interpolation in the geographic position of one or more other data samples that obtain when the ability time spent in the geographic position of determining vehicle.

87. according to the method for claim 52, wherein, described vehicle be its each all be configured to provide data sample to reflect one of a plurality of vehicles that travel of a plurality of vehicles to single traffic information system.

88. 7 method according to Claim 8, wherein, described a plurality of vehicles are parts of the fleet of the work of combining with one another.

89. 7 method wherein, indicates described a plurality of vehicle to operate in different mode according to Claim 8, so that the change in information of the relevant described a plurality of traveling state of vehicle that provided to be provided.

90. according to the method for claim 52, wherein, traffic information system indicates described vehicle circuit as indicated to travel, to obtain the driving information of relevant vehicle along the line.

91. a computer-readable medium, its content can make computing equipment by carrying out the information that a kind of method provides relevant mobile device to move, described method comprises:

The place obtains a plurality of data samples in a plurality of times, and each data sample has been indicated the geographic position at the time place mobile device related with described data sample;

Store described a plurality of data sample; With

Automatically the data sample from described a plurality of storages provides information, so that other mobile device is mobile.

92. computer-readable medium according to claim 91, wherein, described mobile device is related with the vehicle in one or more travels down, and each of at least some of described data sample also indicated the travelling characteristic in the time place associated vehicle related with described data sample.

93. computer-readable medium according to claim 92, wherein, the travelling characteristic of each of described at least some data samples comprises the speed of the associated vehicle of travelling the orientation and being reported of the associated vehicle of being reported, provide information to comprise from the data sample of described a plurality of storages: individual data transmission, described a plurality of data samples to be transferred to traffic information system, with the travelling of being convenient to from the data sample of a plurality of other vehicles on described one or more roads of other vehicle.

94., wherein, carry out described method by the one or more mobile devices that pass through that can detect mobile device and/or vehicle along described one or more roads according to the computer-readable medium of claim 92.

95. computer-readable medium according to claim 91, wherein, described method also comprises, before the data sample from described a plurality of storages provides information, data sample acquisition of information from described a plurality of storages, to reduce the data volume that will be included in the described individual data transmission, the information that is provided comprises the information of collection.

96. computer-readable medium according to claim 91, wherein, described mobile device is at least a portion of described computing equipment, described mobile device has the ability that the visit geo-location is determined, each the geographic position that is used at least some data samples is based on the information that obtains from described mobile device.

97. according to the computer-readable medium of claim 91, wherein, described computer-readable medium is the storer of computing equipment.

98. according to the computer-readable medium of claim 91, wherein, described computer-readable medium is a data transmission media, transmits data-signal that produced, content.

99. according to the computer-readable medium of claim 91, wherein, described content is to make described computing equipment carry out the instruction of described method when being performed.

100. one kind is configured to provide the computing equipment about the driving information of vehicle on one or more roads, comprises:

One or more memory modules; With

Data source information provides assembly, be configured to provide in the following manner driving information: obtain a plurality of data samples at a plurality of different acquisition time places about vehicle on one or more roads, so that each data sample has reflected one or more travelling characteristics of the acquisition time place vehicle of described data sample, the a plurality of data samples that obtain of temporary transient storage in described one or more memory modules, and, be used for long-range traffic information system to assist travelling on one or more roads as one group of data sample that sends described a plurality of storages.

Computing equipment according to claim 100, wherein, described data source information provides assembly also to be configured to, after sending the data sample of a plurality of storages as one group, from described one or more memory modules, remove the data sample of being stored, so that can temporarily store other data sample that is obtained, described data source information provides assembly also to be configured to repeatedly provide the information that vehicle travels of closing by repeatedly obtaining and store data sample and periodically sending the data sample of storing.

Computing equipment according to claim 100, wherein, described computing equipment travels with vehicle, and comprise that the receiver that can obtain GPS (" GPS ") signal, the travelling characteristic that is reflected comprise at least one in the orientation of geographic position, speed and vehicle in each data sample.

Computing equipment according to claim 102, wherein, described computing equipment is the part of system, described system comprises the transmitting set that can send data transmission, comprise as one group of data that send a plurality of storages: use transmitting set sends a plurality of storages in the individual data transmission data sample to long-range traffic information system, so that use to assist travelling of on described one or more roads other vehicle with data sample from a plurality of other vehicles.

According to the computing equipment of claim 100, wherein, described data source information provide assembly to comprise to be used for the storer of described computing equipment for the software instruction of carrying out.

Computing equipment according to claim 100, wherein, described data source information provides assembly to comprise generator, be used for providing in the following manner the driving information of vehicle on one or more roads: obtain a plurality of data samples so that described each data sample has reflected one or more travelling characteristics of the acquisition time place vehicle of described data sample at a plurality of different acquisition times place, a plurality of data samples that temporary transient storage is obtained in described one or more memory modules, and, be used for long-range traffic information system and assist travelling on one or more roads as one group of data sample that sends described a plurality of storages.

A kind of computer-executed method is used to estimate the data sample of reporting by at the vehicle of travels down that described data sample includes the information of closing the vehicle travel conditions, and described method comprises:

Receive the indication of a plurality of road segment segment of one or more roads;

Receive the information of the current traffic condition of relevant described a plurality of road segment segment, the information that is received comprises a plurality of data samples, each data sample all from a plurality of vehicles is reported, and has been reflected the report speed of a described vehicle at the reported position place that states one of road segment segment in the report time place; With

For described a plurality of road segment segment each, in the following manner, estimate the traffic of described road segment segment based on the data sample that is identified the travel conditions of representing described road segment segment:

The group of identification multidata sample from a plurality of data samples is so that the data sample of this group has and the corresponding reported position of the travel conditions of described road segment segment;

For each data sample in this group, determine the average velocity and the standard deviation of all other data samples of this group based on the report speed of these data samples, and based on the difference of the report speed of data sample and difference between determined average velocity and determined standard deviation how, determine whether described data sample is the statistics exceptional value for other data sample of this group;

From this group, remove and be confirmed as adding up the data sample of exceptional value; With

After removing, use remaining data sample all vehicles deduction traffics in this group on road segment segment, travelling,

So that the traffic of inferring based on data sample can be used for assisting travelling on described road segment segment.

Method according to claim 106, wherein in each of a plurality of different time periods, carry out the estimation of traffic at each of a plurality of road segment segment, and wherein also carry out the identification of the group of the data sample of road segment segment in the time period, so that institute's recognition data sample of this group has and corresponding report time of this time period.

Method according to claim 106, wherein, at least one each for a plurality of road segment segment, being confirmed as is the data sample of identification group of a road segment segment of statistics exceptional value, be from the vehicle that travels on another road segment segment and improperly with a described data sample that road segment segment is related.

Method according to claim 106, wherein, for at least one each of a plurality of road segment segment, being confirmed as is the data sample of identification group of a road segment segment of statistics exceptional value, is from being parked on described this road segment segment or the data sample of the vehicle on this road segment segment next door.

110. method according to claim 106, wherein, for each estimation traffics of described a plurality of road segment segment comprises: the average velocity and the standard deviation of all data samples that is identified for institute's identification group of described road segment segment, use is used for the determined average velocity and the standard deviation of all data samples, is identified for the average velocity of all other data samples of this group and the part of standard deviation as each data sample at institute's identification group.

111. method according to claim 110, wherein, whether the data sample of determining group is that the statistics exceptional value comprises with respect to other data sample of this group: based on the determined standard deviation of organizing other data sample at this, determine whether the difference between the average velocity of the report speed of data sample and determined this other data sample of group has exceeded threshold value.

112. method according to claim 106, wherein, repeat to receive the data sample relevant with the current traffic condition of described a plurality of road segment segment, with the change of reflection traffic, carry out the estimation of traffic at the data sample that receives recently to each of described a plurality of road segment segment with real-time mode.

113. method according to claim 112, wherein, remaining data sample infers that the traffic of all vehicles that travel comprises in the use group on road segment segment: determine average velocity at remaining data sample, infer average velocitys based on determined average velocity at all vehicles that on described road segment segment, travel, and the information of the average velocity of relevant deduction is offered one or more people that consideration will be travelled on described road segment segment.

114. a computer-executed method is used to estimate the data sample of representative at the vehicle of travels down, described method comprises:

Receive the indication of one or more road segment segment of one or more roads, every road segment segment all has a plurality of associated data samples of each report speed that has reflected the vehicle on described road segment segment; With

At least one each for described road segment segment:

Automatically analyze a plurality of associated data samples of this road segment segment, to determine not represent the one or more of actual vehicle travel conditions on described road segment segment in those data samples, at least one of determined data sample is the statistics exceptional value with respect to other data sample in a plurality of associated data samples; With

Other data sample provide one or more indications from follow-up use, to remove determined data sample, so that can be used for assisting travelling on described road segment segment.

115. method according to claim 114, wherein, one or more at least one road segment segment, providing indication to remove determined data sample from follow-up use comprises: the associated data sample of analyzing the road segment segment except that determined data sample, average velocity with the vehicle determining on road segment segment, to travel, indicate determined average velocity, to assist travelling of on road segment segment other vehicle.

116. method according to claim 114, wherein, one or more at least one road segment segment, providing indication to remove determined data sample from follow-up use comprises: analyze the road segment segment associated data sample except that determined data sample, the magnitude of traffic flow with the vehicle determining on road segment segment, to travel, indicate the determined magnitude of traffic flow, to assist travelling of on road segment segment other vehicle.

117. method according to claim 114, wherein, one or more at least one road segment segment, determine not represent one or more data samples of the actual vehicle travel conditions on the road segment segment to comprise at road segment segment: each that determine these data samples is the statistics exceptional value with respect to other data sample in a plurality of data samples related with described road segment segment.

118. method according to claim 117, wherein, carry out following steps by removing an exceptional value analysis: determine the data sample related with road segment segment be with respect to the related a plurality of data samples of described road segment segment in the statistics exceptional value of other data sample.

119. method according to claim 117, wherein, following steps are at least in part based on report speed that each described data sample reflected: determine the data sample related with road segment segment be with respect to the related a plurality of data samples of described road segment segment in the statistics exceptional value of other data sample.

120. according to the method for claim 117, wherein, each that determine the one or more data samples related with road segment segment be with respect to the related a plurality of data samples of described road segment segment in the statistics exceptional value of other data sample comprise:

Determine average velocity and standard deviation at a plurality of data samples of road segment segment whole; With

At each of one or more data samples of road segment segment,

Based on determined average velocity of whole a plurality of data samples and standard deviation, determine the average velocity and the standard deviation of all other data samples of road segment segment at road segment segment;

The report speed of determining data sample and at poor between the determined average velocity of all other data samples in a plurality of data samples of road segment segment;

Determine threshold value based on the standard deviation of all other data samples in a plurality of data samples of determined road segment segment at least in part; With

When determined difference exceeds determined threshold value, discern this data sample and be the statistics exceptional value.

121. method according to claim 117, wherein, for described one or more road segment segment each, carry out following steps in real-time basically mode: each of one or more data samples of determining described road segment segment is the statistics exceptional value with respect to other data sample in a plurality of data samples related with road segment segment.

122. method according to claim 114, wherein, one or more for described at least one road segment segment, determine to represent one or more data samples of the road segment segment of the actual vehicle travel conditions on the road segment segment to comprise: to estimate that at least one determined data sample has reflected the behavior of every vehicle of its report speed, and determine the behavior of every vehicle being estimated and do not correspond to actual vehicle travel conditions on road segment segment.

123. according to the method for claim 122, wherein, the estimation behavior of at least one vehicle is corresponding to the vehicle that stops.

124. according to the method for claim 122, wherein, based on the behavior of at least one vehicle of one or more determined data samples corresponding to the travel conditions on the road segment segment except that the road segment segment related with one or more determined data samples.

125. method according to claim 114, wherein, one or more at least one road segment segment, definite one or more data samples of the road segment segment of actual vehicle travel conditions on the road segment segment of can not representing comprise: a plurality of data samples that identification is reported by the single portion vehicle that travels on described road segment segment, determine that based on institute's recognition data sample this time place states the behavior of single portion vehicle, and determine that based on determined behavior institute's recognition data sample do not represent the actual vehicle travel conditions on road segment segment.

126. method according to claim 114, wherein, one or more at least one road segment segment, definite one or more data samples of the road segment segment of the actual vehicle travel conditions on the road segment segment of can not representing comprise: discern the desired value of a plurality of data samples related with described road segment segment, and determine that determined data sample does not meet the desired value of being discerned.

127. method according to claim 114, wherein, one or more at least one road segment segment, definite one or more data samples of the road segment segment of the actual vehicle travel conditions on the road segment segment of can not representing comprise: determine the statistical distribution of a plurality of data samples related with described road segment segment, and determine that determined data sample does not meet determined statistical distribution.

128. method according to claim 114, wherein, one or more at least one road segment segment, definite one or more data samples of the road segment segment of the actual vehicle travel conditions on the road segment segment of can not representing comprise: a plurality of different data and curves of discerning described road segment segment, each data and curves has reflected the different subclass of the traveling state of vehicle at least a portion of described road segment segment, and determined data sample meets at least one data and curves of the subclass that has reflected uninterested traveling state of vehicle.

129. according to the method for claim 128, wherein, at least one of institute's recognition data curve is Gaussian curve.

130. method according to claim 114, wherein, one or more at least one road segment segment, each of a plurality of associated data samples of described road segment segment also reflected the corresponding report time of report speed with the vehicle of described data sample, the automatic analysis of a plurality of associated data samples of described road segment segment is also corresponding to the preset time section, so that the actual vehicle travel conditions is the travel conditions in the section at the fixed time on described road segment segment.

131. method according to claim 130, wherein, one or more at least one road segment segment, determine to represent one or more data samples of the road segment segment of the actual vehicle travel conditions on the road segment segment to comprise: the report time of determined each data sample of identification is not in being used for the predetermined amount of time of described road segment segment.

132. the method according to claim 114 also comprises, for each of a plurality of different time periods, receive a plurality of associated data samples of one of described road segment segment, each associated data sample standard deviation has reflected that in time period report time place states the report speed of the vehicle on this road segment segment, based on the data sample of its report time in the described time period, be that described this road segment segment is carried out automatically and analyzed in each of described time period.

133. method according to claim 114, wherein, one or more at least one road segment segment carry out following steps in real-time basically mode: one or more data samples of determining to represent the road segment segment of the actual vehicle travel conditions on the road segment segment.

134. method according to claim 133, wherein, obtain by the vehicle that on these road segment segment, travels with at least some of at least some related a plurality of data samples of described road segment segment and by described vehicle report, after obtaining described at least one data sample one or more, the report that produces described at least some data samples in real-time basically mode.

135. a computer-readable medium, its content can make the computing equipment estimation represent the data sample of driving vehicle by carrying out following method, and described method comprises:

Receive the indication of a plurality of data samples, each data sample has reflected one report travelling characteristic in a plurality of vehicles that travel on road;

Automatically determine to represent one or more data samples of the actual vehicle of on road segment segment, travelling; With

Provide one or more indications of the described data sample except that determined data sample, so that indicated data sample can be so that travelling on the road.

136. computer-readable medium according to claim 135, wherein, the report travelling characteristic of each data sample comprises and the corresponding car speed of data sample, definite one or more data samples of the road segment segment of the actual vehicle of travelling on the road segment segment of can not representing comprise: determine that at least one data sample is the statistics exceptional value with respect to other data sample, provide the indication of data sample to comprise: to analyze data sample determine the to travel average velocity of the vehicle on road, and indicate determined average velocity, so that the travelling of other vehicle on the road.

137., wherein, determine that at least one data sample is that the statistics exceptional value comprises with respect to other data sample: carry out in real-time basically mode and remove an exceptional value analysis according to the computer-readable medium of claim 136.

138. according to the computer-readable medium of claim 135, wherein, described computer-readable medium is the storer of computing equipment.

139. according to the computer-readable medium of claim 135, wherein, described computer-readable medium is a data transmission media, transmits data-signal that produced, content.

140. according to the computer-readable medium of claim 135, wherein, described content is to make computing equipment carry out the instruction of described method when being performed.

141. a computing system that is configured to estimate the data sample of representing driving vehicle comprises:

First assembly is configured to every at a plurality of roads, receives the indication of a plurality of data samples of described road, and each data sample is reflected in the report speed of the vehicle of described travels down; With

The data sample exceptional value is removed assembly, is configured to every at a plurality of roads:

Automatically a plurality of data samples one or more that determine described road are the statistics exceptional values with respect to other data sample in a plurality of data samples of described road; With

Indicated data sample provides one or more indications of a plurality of data samples of the described road except that determined data sample, so that can be assisted travelling on road.

142. computing equipment according to claim 141, wherein, at least one each for a plurality of roads, the data sample of determining road is that the statistics exceptional value comprises with respect to other data sample of the data sample of described road: carry out in real-time basically mode and remove an exceptional value analysis, provide the indication of the data sample of described road to comprise: to analyze the average velocity of described data sample with the vehicle on described road of determining to travel, and indicate determined average velocity, to assist travelling of on described road other vehicle.

143. according to the computing system of claim 141, wherein, described first assembly and described data sample exceptional value are removed assembly and are included the software instruction for execution that is used at the storer of described computing system.

144. computing system according to claim 141, wherein, described first assembly comprises receiving trap, be used for every at a plurality of roads, receive the indication of a plurality of data samples of described road, each data sample has reflected the report speed at the vehicle of described travels down, described data sample exceptional value is removed assembly and is comprised device, be used to carry out following operation: at every of a plurality of roads, automatically a plurality of data samples one or more that determine described road are the statistics exceptional values with respect to other data sample of a plurality of data samples of described road, and provide except that determined data sample, one or more indications of a plurality of data samples of described road are so that indicated data sample can be assisted travelling on road.

145. a computer-executed method is used for determining the estimation traffic flow information based on the data sample of being reported by the vehicle of travels down that described data sample comprises the data of relevant described vehicle traveling information, described method comprises:

Receive the indication of a plurality of road segment segment in one or more roads;

At in the time period on road segment segment each of a plurality of different vehicle observed quantities produce probability distribution, described probability distribution has been indicated under given vehicle observed quantity the probability of vehicle arrival rate on described road segment segment; With

For each of a plurality of time periods:

In the described time period, receive the information of the current traffic condition of relevant a plurality of road segment segment, the information that is received comprises a plurality of data samples in the described time period, a described data sample of report from a plurality of vehicles, each data sample has reflected that the report time in the described time period is in the report speed of a vehicle at the reported position place of a described road segment segment, described a plurality of vehicle is the subclass of all vehicles of travelling on described road segment segment in the described time period, the information that is received also comprises a plurality of additional data samples in the described time period, a described additional data sample of report from a plurality of traffic sensors that monitor described a plurality of road segment segment, each additional data sample has reflected the report speed based on one or more velocity readings of the one or more vehicles in position of the described road segment segment in inherent one or more report times place of described time period;

For every of a plurality of road segment segment, estimate the traffic flow information of all vehicles on described road segment segment that travel in the described time period as follows automatically:

Discern the group of the multidata sample of described road segment segment in the described time period, described multidata sample is from a plurality of data samples and a plurality of additional data sample at least one;

Determine and described group the corresponding vehicle number of data sample that described corresponding vehicle is the subclass of all vehicles on described road segment segment of travelling in the described time period;

Determine all vehicles of travelling on the inherent described road segment segment of described time period most probable traffic arrival rate, determine that the traffic arrival rate is at least in part based on the probability distribution that is produced at the determined vehicle fleet size of data sample of described group of report at described road segment segment place;

Determine the most probable traffic density of described road segment segment, so that represent the total amount of the per unit distance of all vehicles that travel on the inherent described road segment segment of described time period, determine that traffic density is at least in part based on the traffic arrival rate of described road segment segment in the determined described time period; With

Determine all vehicles of travelling on the inherent described road segment segment of described time period described road segment segment locate most probable number percent traffic occupancy more at least, determine that number percent traffic occupancy is at least in part based on determined traffic density; With

Use traffic arrival rate, traffic density and number percent traffic occupancy in the determined described time period to assist travel the future on described one or more roads,

Thereby come to determine the estimation traffic flow information for described road segment segment based on the data sample that has reflected the actual vehicle travel conditions on described road segment segment.

146. method according to claim 145, wherein, at least one each at least one and the described road segment segment of described time period, the identification group of a plurality of data samples of described road segment segment comprises in the described time period: from the multidata sample of a plurality of data samples in the described time period, the reported position of described data sample is positioned on the described road segment segment; From a plurality of additional data samples in the described time period, its position is positioned at the data sample of described road segment segment, estimates that the traffic flow information of all vehicles that travel on the inherent described road segment segment of described time period comprises: the average traffic speed of estimation that is created in all vehicles that travel on the inherent described road segment segment of described time period at least in part based on the report speed of described group data sample.

147. method according to claim 146, wherein, for each of at least one described time period and each of at least one described road segment segment, at the traffic density of in the described time period, determining described road segment segment at all vehicles that travel on the described road segment segment also at least in part based on the average traffic speed of estimation of all vehicles that travel on the inherent described road segment segment of described time period.

148. method according to claim 147, wherein, for each of at least one described time period and each of at least one described road segment segment, determine in the described time period at all vehicles that travel on the described road segment segment for the number percent traffic occupancy more at least of described road segment segment also at least in part based on the estimation average length of all vehicles that travel on the average traffic speed of estimation of all vehicles that travel on the inherent described road segment segment of described time period and the inherent described road segment segment of described time period.

149. method according to claim 148, wherein, for each of at least one described time period and each of at least one described road segment segment, determine all vehicles of travelling on the inherent described road segment segment of described time period described road segment segment place the traffic arrival rate also at least in part based on: in all vehicles that travel on the inherent described road segment segment of described time period with the estimation number percent of described group the corresponding vehicle of data sample.

150. method according to claim 145, wherein, for each of each and described at least one road segment segment of described at least one time period, the traffic arrival rate of determining all vehicles of travelling on the inherent described road segment segment of described time period comprises: produce the value of the confidence to be reflected in the degree of possible errors in the described traffic arrival rate at described traffic arrival rate, use the value of the confidence that at least some determined traffic arrival rate comprise that use produces with auxiliary following travelling in the described time period.

151. method according to claim 145, wherein, use the data sample of recent reception to estimate to travel in the described time period traffic flow information of all vehicles on described road segment segment in real-time mode, also in real-time basically mode, use at least some determined traffic arrival rate, determined traffic density and determined number percent traffic occupancy in the described time period, to assist travelling of on described one or more roads, being about to.

152. method according to claim 151, wherein, use at least some determined traffic arrival rate, determined traffic density and determined number percent traffic occupancy in the described time period to comprise: will offer consideration about the information of determined traffic arrival rate, traffic density and number percent traffic occupancy will be one or more people of described one or more travels down.

153. a computer-executed method is used for based on the data sample that has reflected the travels down situation, at the traffic flow information of determining estimation at the vehicle of these travels down, described method comprises:

Receive the indication in one or more highway sections of one or more roads, each road segment segment has the data sample of a plurality of associations, and each data sample is by the report of one of a plurality of vehicles, with the travel conditions on the road segment segment that is reflected in this vehicle place, report time place;

For at least one road segment segment each, automatically come the magnitude of traffic flow of the vehicle that travels on the inherent road segment segment of evaluation time section as follows:

The group of the multidata sample that identification is related with the road segment segment that report time occurred in the described time period;

Determined to report the vehicle fleet size of this group data sample, the vehicle of wherein having reported this group data sample is the subclass of all vehicles of travelling on road segment segment in the described time period;

To small part based on the determined vehicle fleet size of reporting data sample, the estimation total quantity of all vehicles that probabilistic estimation was travelled on road segment segment in this time period; And

Use one or more in the estimation total quantity of vehicle to assist travelling on one or more roads.

154. method according to claim 153, wherein, one or more for described at least one road segment segment, the probabilistic estimation in the estimation total quantity of all vehicles that travel on the described road segment segment in the described time period comprises: the most probable total amount of determining all vehicles of travelling in the described time period on described road segment segment.

155. method according to claim 153, wherein, one or more for described at least one road segment segment, the probabilistic estimation of the estimation total quantity of all vehicles that travel on described road segment segment in the described time period comprises: the value of the confidence that is identified for estimating total quantity at least in part based on the possibility of estimation total quantity.

156. method according to claim 153, wherein, one or more for described at least one road segment segment, probabilistic estimation in the estimation total quantity of all vehicles that travel on the described road segment segment in the described time period comprises: determine the traffic arrival rate of getting on the bus more at least of the road segment segment in the described time period, determined traffic arrival rate is at least in part based on the estimation total quantity of all vehicles that travel on the inherent described road segment segment of described time period.

157. method according to claim 156, wherein, for each of described one or more road segment segment, the determining of the traffic arrival rate of getting on the bus more at least of the road segment segment in the described time period comprises that probability of use distributes, and this probability distribution representative is at the given determined probability of the actual arrival rate of vehicle when reporting described group the vehicle fleet size of data sample for road segment segment.

158. method according to claim 157, wherein, for described one or more road segment segment each, use described probability distribution to comprise: to determine most probable traffic arrival rate as a part of determining the traffic arrival rate of the vehicle more at least of road segment segment in the described time period.

159. method according to claim 157, wherein, for described one or more road segment segment each, use described probability distribution to comprise: based on the confidence level of described probabilistic distribution estimation in determined traffic arrival rate as a part of determining the traffic arrival rate of the vehicle more at least of road segment segment in the described time period.

160. according to the method for claim 157, wherein, for described one or more road segment segment each, described probability distribution is Poisson distribution.

161. method according to claim 156, wherein, for each of described one or more road segment segment, road segment segment in the described time period more at least on determine vehicle the traffic arrival rate comprise the permeability factor of use described road segment segment in the described time period, described permeability factor has been represented in all vehicles that travel on described road segment segment in the described time period, reports the estimation percentage of vehicle of described group data sample for described road segment segment.

162. according to the method for claim 156, wherein, for described one or more road segment segment each, be used for determining vehicle the traffic arrival rate road segment segment be the starting point of described road segment segment more at least.

163. according to the method for claim 156, wherein, for described one or more road segment segment each, be used for determining vehicle the traffic arrival rate road segment segment be all road segment segment more at least.

164. method according to claim 153, wherein, one or more for described at least one road segment segment, the probabilistic estimation of the estimation total quantity of all vehicles that travel on the inherent described road segment segment of described time period comprises: determine the traffic density of described road segment segment in the described time period, determined traffic density is at least in part based on the estimation total quantity of all vehicles that travel on described road segment segment in the described time period.

165. according to the method for claim 164, wherein, for each of described one or more road segment segment, the total quantity of the per unit distance of all vehicles that determined traffic density representative was travelled on described road segment segment in the described time period.

166. according to the method for claim 164, wherein, for each of described one or more road segment segment, to the traffic arrival rate determined at least in part of the traffic density of described road segment segment based on described road segment segment at least one determined described time period.

167. method according to claim 166, also comprise each for described one or more road segment segment, determine at least one traffic arrival rate of described road segment segment in the described time period, so that be illustrated in the vehicle more at least that arrives described road segment segment in the described time period.

168. method according to claim 166, wherein, for each of described one or more road segment segment, to the average traffic speed of estimation determined also at least in part of the traffic density of described road segment segment based on all vehicles that travel on the inherent described road segment segment of described time period.

169. according to the method for claim 168, also comprise each, the average traffic speed of all vehicles that estimation is travelled on described road segment segment in the described time period for described one or more road segment segment.

170. according to the method for claim 164, wherein, for each of described one or more road segment segment, determining to comprise and determine most probable traffic density the traffic density of described road segment segment.

171., wherein,, the traffic density of described road segment segment determined to comprise the confidence level of estimating determined traffic density for each of described one or more road segment segment according to the method for claim 164.

172. method according to claim 153, wherein, one or more for described at least one road segment segment, probabilistic estimation in the estimation total quantity of all vehicles that travel on the described road segment segment in the described time period comprises: determine the traffic occupancy more at least on the inherent described road segment segment of described time period, determined traffic occupancy is at least in part based on the estimation total quantity of all vehicles that travel on described road segment segment in the described time period.

173. method according to claim 172, wherein, for each of described one or more road segment segment, more at least the traffic occupancy of determined described road segment segment was represented in the described time period, number percent averaging time that described at least one vehicle that is travelled on described road segment segment more at least takies.

174. method according to claim 172, wherein, for each of described one or more road segment segment, to determining at least in part of the traffic occupancy of described road segment segment based at least one estimating vehicle length of the vehicle that travels on the inherent described road segment segment of at least one traffic density of described road segment segment and described time period in the determined described time period.

175. method according to claim 174, wherein, for each of described one or more road segment segment, at least one traffic arrival rate determined also at least in part of the traffic occupancy of described road segment segment based on described road segment segment in the determined described time period.

176. method according to claim 175, wherein, for each of described one or more road segment segment, to the average traffic speed of estimation determined also at least in part of the traffic occupancy of described road segment segment based on all vehicles that travel on the inherent described road segment segment of described time period.

177. method according to claim 176, also comprise each for described one or more road segment segment, determine at least one traffic density of described road segment segment in the described time period, determine at least one traffic arrival rate of described road segment segment in the described time period, and estimate the average traffic speed of all vehicles that in the described time period, on described road segment segment, travel.

178. according to the method for claim 172, wherein, for each of described one or more road segment segment, determining to comprise and determine most probable traffic occupancy the traffic occupancy of described road segment segment.

179., wherein,, the traffic occupancy of described road segment segment determined to comprise the confidence level of estimating determined traffic occupancy for each of described one or more road segment segment according to the method for claim 172.

180. method according to claim 153, wherein, for each of one or more road segment segment of described at least some road segment segment, carry out estimation to the magnitude of traffic flow of the vehicle that in the described time period, on described road segment segment, travels in each of a plurality of different time periods.

181. method according to claim 153, wherein, for each of one or more road segment segment of described at least some road segment segment, a plurality of overlapping time of described time period window each in carry out estimation to the magnitude of traffic flow of the vehicle that in the described time period, on described road segment segment, travels so that use at least some of associated data sample of described road segment segment for each of described a plurality of time windows.

182. according to the method for claim 181, wherein, for described one or more road segment segment at least one, be modified in the described time period a plurality of overlapping time window to reflect one or more current situations.

183. method according to claim 153, wherein, for each of one or more road segment segment of described at least some road segment segment, estimation to the total quantity of all vehicles of travelling on described road segment segment in the described time period comprises: be identified for estimating at least one the value of the confidence of total quantity, and wherein the use of one or more estimation total quantitys of vehicle comprised and use the one or more of determined the value of the confidence to assist travel the future on one or more roads.

184. method according to claim 153, wherein, the use of one or more estimation total quantitys of vehicle comprised indication to one or more estimation total quantitys of vehicle to one or more people is provided, with auxiliary people considering to travelling on described one or more roads.

185. method according to claim 184, wherein, in the real-time basically mode of data sample that is used to estimate with respect to reception, carry out to the estimation of one or more estimation total quantitys with to the use of one or more estimation total quantitys, so that allow the people can carry out real-time considering basically.

186. it is the vehicle estimation traffic flow information that travels by carrying out following method that a computer-readable medium, its content can make computing equipment, described method comprises:

Receive the indication of a plurality of data samples, each data sample is reflected in one of multi-section vehicle on the road travelling;

At least in part based on the quantity that reflects the multi-section vehicle that it travels by described data sample, in the magnitude of traffic flow at all vehicles of described travels down in the time period of estimation on the probability; With

The indication that the magnitude of traffic flow of being estimated is provided is with travelling on the service road.

187. computer-readable medium according to claim 186, wherein, each all has the information of the vehicle ' of report time on described road that relates in the described time period to each of described a plurality of data samples by vehicle report and its, estimation in the magnitude of traffic flow of all vehicles of described travels down comprises: the multi-section vehicle of determining the described data sample of report, the vehicle of reporting described data sample be in the described time period in the subclass of all vehicles of described travels down, and comprise at least in part the possibility of determining the arrival rate of the arrival rate of the estimation of any on the road of all vehicles and estimation based on the determined multi-section vehicle of the described data sample of report.

188. computer-readable medium according to claim 187, wherein, also comprise by the vehicle on described road of in the described time period, travelling in the estimation of the magnitude of traffic flow of all vehicles of described travels down and to determine on road some average percent occupancy at place, determining at least in part based on determined arrival rate of described average percent occupancy provides the indication of the magnitude of traffic flow of described estimation to comprise that the driver to vehicle represents that the information of the relevant magnitude of traffic flow of being estimated influences considering of relevant travels down to be suitable for so that be used for being convenient to travelling on road.

189. according to the computer-readable medium of claim 186, wherein, described computer-readable medium is the storer of computing equipment.

190. according to the computer-readable medium of claim 186, wherein, described computer-readable medium data transmission media transmits data-signal that produced, that comprise described content.

191. according to the computer-readable medium of claim 186, wherein, described content is to make described computing equipment carry out the instruction of described method when being performed.

192. a computing system that is configured to estimate the traffic flow information of driving vehicle comprises:

First assembly is configured to each at a plurality of roads, receives the indication of a plurality of data samples related with road, and each data sample all comprises the information of having represented the traveling state of vehicle on road; With

Data sample flow estimation assembly is configured to each at a plurality of roads,

Determine the vehicle fleet size of its travel conditions by the information representative of the data sample related with road;

Based on determined vehicle fleet size, be created in the probabilistic estimation of the magnitude of traffic flow of all vehicles on described road that travel in certain time period at least in part; With

Provide to the estimation magnitude of traffic flow indication to be used for travelling on the service road.

193. computing system according to claim 192, wherein, for in a plurality of roads at least some each, in a plurality of data samples that are associated with road each is all reported by vehicle, thereby the information that comprises in the data sample is the travel conditions information about vehicle on the road at the report time place in the time period of described road, wherein its travel conditions is the subclass of all vehicles of the inherent described travels down of time period of described road by the vehicle of the information representative of the data sample related with described road, generation in probabilistic estimation of the magnitude of traffic flow of all vehicles of described travels down is comprised: at least in part based on the quantity of determined its travel conditions by the vehicle of the information representative of the data sample related with road, determine the estimation arrival rate of all vehicles certain point on road and the possibility of estimation arrival rate, and provide indication to the estimation magnitude of traffic flow: be that the driver of vehicle represents that the information of the relevant estimation magnitude of traffic flow is to be used to influence the decision of relevant road driving to be used to assisting travelling on described road to comprise.

194. computing system according to claim 193, wherein, for each of at least some roads, the probabilistic estimation of the magnitude of traffic flow that is created in all vehicles of described travels down also comprises: by in the time period of described road, determining the average percent occupancy of certain point on described road at the vehicle of described travels down, and the determining of average percent occupancy at least in part based on determined estimation arrival rate.

195. according to the computing system of claim 192, wherein, each of described first assembly and described data sample flow estimation assembly be included in the storer of described computing system for the software instruction of carrying out.

196. computing system according to claim 192, wherein, described first assembly comprises receiving trap, it is a plurality of roads each, reception is to the indication of a plurality of data samples of being associated with described road, each data sample all comprises the information of the traveling state of vehicle of representative on road, described data sample flow estimation assembly comprises device, it is a plurality of roads each, determine the quantity of its travel conditions by the vehicle of the information representative of the data sample related with road, be created in the probabilistic estimation of the magnitude of traffic flow of all vehicles of inherent travels down of time period at least in part based on determined vehicle fleet size, and provide the indication of the estimation magnitude of traffic flow so that be used for travelling on the service road.

197. a computer-executed method is used to estimate the data sample of reporting by at the vehicle of travels down, this data sample includes the information of closing the vehicle travel conditions, and described method comprises:

Reception is to the indication of a plurality of road segment segment of one or more roads;

Receive the information of the current traffic condition of relevant a plurality of road segment segment, the information that is received comprises a plurality of data samples, each data sample is all by a report in a plurality of vehicles, and reflects the report speed of this vehicle in the report geographical location, the report that also reflects this vehicle orientation of travelling; With

For each of a plurality of road segment segment,, based on being identified the data sample of representing the travel conditions on the described road segment segment, and be described road segment segment estimation traffic by following steps:

One group of multidata sample of identification from a plurality of data samples, the geographic position that this group data sample is reported in preset distance with respect to one or more predetermined geographic localities of described road segment segment, and this group data sample reported travel the orientation in target offset with respect to one or more preset bearings of described road segment segment;

At least in part based on determined not with the report geographic position of the corresponding data sample of predetermined portions of the road segment segment at interested traveling state of vehicle place, one or more data samples of determining this group are not automatically represented the actual vehicle travel conditions on the described road segment segment;

Remove the determined data sample of not representing the actual vehicle travel conditions on the described road segment segment from described group; With

After removal, use the traffic of all vehicles that remaining data sample deduction is travelled in group on described road segment segment,

Make so that can obtain the traffic of inferring based on data sample and to be used for assisting travelling on described road segment segment.

198. method according to claim 197, wherein, a road segment segment of a plurality of road segment segment is corresponding to the first of the expressway with a plurality of tracks, the predetermined geographic locality that wherein is used for a described road segment segment comprises the geographic area in a plurality of tracks that cover the expressway that is used for described first, the preset distance that extend described geographic area is at least in part based on the precision of the sort of location determining device in the report geographic position of at least some data samples that are used to be defined as the group that a described road segment segment identifies, the preset bearing that wherein is used for a described road segment segment comprises the corresponding one or more orientation of direction of the vehicle that travels on a plurality of tracks with the first of described expressway, and at least in part based on the degree of accuracy of position determining device, at least some data samples that described position determining device is used for the group that identifies at a described road segment segment are determined the report orientation of travelling with respect to the target offset of one or more preset bearings of a described road segment segment.

199. method according to claim 198, wherein, the predetermined portions of a described road segment segment at interested traveling state of vehicle place comprise one or more tracks of expressway, can not represent its report geographic position in the data sample of group of the actual vehicle travel conditions on a described road segment segment to be confirmed as and the corresponding data sample in ramp above and/or under the expressway for being confirmed as of identifying of a described road segment segment.

200. method according to claim 198, wherein, the predetermined portions of a described road segment segment at described interested traveling state of vehicle place comprises one or more tracks of described expressway, and being confirmed as of wherein identifying for a described road segment segment can not to represent in the data sample of group of the actual vehicle travel conditions on a described road segment segment one be that its report geographic position is confirmed as and cross/near bifurcated road or the expressway the corresponding data sample in track.

Method according to claim 198, wherein, the predetermined portions of a described road segment segment at described interested traveling state of vehicle place comprises the subclass in the track of described expressway, and being confirmed as of wherein identifying for a described road segment segment can not represent in the data sample of group of the actual vehicle travel conditions on a described road segment segment one be its report geographic position be confirmed as with subclass not in the track in the corresponding data sample in track of expressway.

Method according to claim 198, wherein, for being confirmed as of identifying of a described road segment segment can not represent in the data sample of group of the actual vehicle travel conditions on a described road segment segment one be from travel with corresponding another road segment segment of the different second portion of described expressway on vehicle and be confirmed as and a described data sample that road segment segment is related improperly.

Method according to claim 198, wherein, the type of described location determining device is GPS (GPS) device type, and the preset distance that extend described geographic area is corresponding to such distance, is accurate at the reading of the interior GPS equipment from described type of this distance.

Method according to claim 197, wherein, one or more each for described a plurality of road segment segment, automatically determine one or more data in the group do not represent the actual vehicle travel conditions on the described road segment segment also at least in part based on: report the one or more ride characteristics except the report geographic position of the vehicle of those data samples, described one or more ride characteristics comprise the car speed that the report speed of those data samples is reflected.

Method according to claim 197, wherein, each of a plurality of data samples also indicated and the report speed of data sample, report geographic position and the report report time that the orientation is associated that travels, wherein carry out each the estimation of traffic of a plurality of road segment segment in each of a plurality of different time periods, also carry out in the time period identification, have report time corresponding to the described time period with the data of the group of toilet identification to the group of the data sample of road segment segment.

Method according to claim 197, wherein, receive the data sample relevant repeatedly to reflect changing traffic with the current traffic condition of described a plurality of road segment segment, serve as that the data sample that recently receives is carried out the estimation of traffic to each of described a plurality of road segment segment wherein in real-time mode, use in described group remaining data sample to infer that the traffic of all vehicles that travel comprises: the average velocity of determining remaining data sample on described road segment segment, infer the average velocity of all vehicles that on described road segment segment, travel based on determined average velocity, and the information of relevant average velocity of inferring is offered the people that one or more considerations will be travelled on described road segment segment.

A kind of computer-executed method is used to estimate the data sample of having represented at the vehicle of travels down, and described method comprises:

Receive the indication of one or more road segment segment of one or more roads, each road segment segment all has the data sample of a plurality of associations, and each data sample is all by one in multi-section vehicle report and indicated reported position with the corresponding vehicle of described road segment segment; With

For at least one each of described road segment segment,

Automatically analyze a plurality of associated data samples of this road segment segment, determine not represent in those data samples the one or more of on described road segment segment actual vehicle travel conditions, each of at least one of determined data sample indicated the reported position of the vehicle of report data sample, and this report position is corresponding to the actual vehicle travel conditions on described road segment segment, and at least one each of determined data sample all have the described data sample of report vehicle related orientation and should the association orientation not corresponding to the actual vehicle travel conditions on described road segment segment; With

Other data sample provide one or more indications from follow-up use, to remove determined data sample, so that can be used for assisting travelling on described road segment segment.

Method according to claim 207, wherein, one or more each at least one road segment segment, provide indication to remove determined data sample and comprise the associated data sample except determined data sample of analyzing the highway section for follow-up use, with the average velocity of the vehicle determining on road segment segment, to travel, and comprise that the determined average velocity of indication is to be used for assisting travelling of on road segment segment other vehicle.

Method according to claim 208, wherein, for each of one or more road segment segment, providing indication to remove determined data sample for follow-up use comprises: the associated data sample except determined data sample of analyzing the highway section, with the magnitude of traffic flow of the vehicle determining on road segment segment, to travel, and indicate the determined magnitude of traffic flow to be used for assisting travelling of on road segment segment other vehicle.

210. method according to claim 207, wherein, for one or more road segment segment each, determine that the one or more data samples at the actual vehicle travel conditions on the road segment segment of not representing of road segment segment comprise: the reporting vehicle position of determining those data samples is corresponding to the uninterested segment path of representing the actual vehicle travel conditions on the road segment segment.

211. according to the method for claim 210, wherein, at least one each of one or more road segment segment, described road part is to go to and/or from the part of the low capacity road of described road segment segment at least.

212. according to the method for claim 210, wherein, at least one each of described one or more road segment segment, described road partly is near the part of the different road described road segment segment.

213. according to the method for claim 210, wherein, at least one each of described one or more road segment segment, described road partly is the subclass as a plurality of tracks of a described road segment segment part.

214. method according to claim 210, wherein, for at least one each of described one or more road segment segment, described road partly be up and/or down ramp of described road segment segment, with related the crossing of the road of described road segment segment/bifurcated road, with the road of described road segment segment related cross and/or the bifurcated track, with the curb of the road of the related branch line track of the road of described road segment segment, described road segment segment be used for one or more at least a portion of fault zone of the road of described road segment segment.

215. according to the method for claim 210, wherein, for one of described one or more road segment segment, described uninterested segment path is the part of a described road segment segment.

216. according to the method for claim 210, wherein, for one of described one or more road segment segment, described uninterested segment path is at least a portion with different another road segment segment of a described road segment segment.

217. method according to claim 207, wherein, for of described at least one road segment segment, some of a plurality of data samples related with a described road segment segment are also related with one or more other different road segment segment, and the determined one or more data samples of a described road segment segment that are used for are from described at least some data samples.

218. method according to claim 207, also comprise one for described road segment segment, at least in part based on and a uninterested road segment segment, automatically be identified for the data sample of a plurality of associations of a described uninterested road segment segment, and provide one or more indications to come to remove the data sample of described a plurality of associations for follow-up use.

219. according to the method for claim 218, wherein, at least in part based on determining that as uninterested function road class a described road segment segment is uninterested.

220., wherein, determine that based on the actual vehicle volume of traffic on a described road segment segment a described road segment segment is uninterested at least in part according to the method for claim 218.

221. method according to claim 218, wherein, at least in part based on to determining and/or, determine that a described road segment segment is uninterested at (intra-day) variable quantity on the same day of vehicular traffic on the described road segment segment to the determining of (inter-day) the in the daytime variable quantity of vehicular traffic on a described road segment segment.

222. according to the method for claim 218, wherein, at least in part based on the actual traffic amount of blocking up on a described road segment segment is determined that a described road segment segment is uninterested.

223. method according to claim 218, wherein, at least in part based on to traffic congestion on the described road segment segment when daily variation determine and/or to the variable quantity in the daytime of traffic congestion on a described road segment segment determine determine that a described road segment segment is uninterested.

224., also comprise and determine not related one or more data samples automatically, and provide one or more indications to come to remove described one or more data sample for follow-up use with uninterested any road segment segment according to the method for claim 207.

225. method according to claim 207, wherein, for one or more each of described at least one road segment segment, to one or more data samples of not representing the actual vehicle travel conditions on described road segment segment of road segment segment determine comprise: determine under-represented one or more data samples based on the reported position of one or more data samples at least in part.

226. method according to claim 225, wherein, for each of described one or more road segment segment, each of a plurality of associated data samples of described road segment segment all indicated the speed of the vehicle of report data sample, and determining also at least in part based on by the indicated speed of described one or more data samples under-represented one or more data samples of described road segment segment.

227. method according to claim 226, also be included as at least one each of described one or more road segment segment, be at least some each of a plurality of associated data samples of described road segment segment, by using the indication speed of estimating described data sample by the reported position of a plurality of data samples indications of the vehicle report of the described data sample of report.

228. method according to claim 225, wherein, for each of described one or more road segment segment, be used for described road segment segment a plurality of associated data samples each all have the related orientation of vehicle of the described data sample of report, and one or more data samples of wherein determining described road be not representative also at least in part based on the related orientation of described one or more data samples.

229. method according to claim 228, also be included as at least one each of described one or more road segment segment, be at least some each of a plurality of associated data samples of being used for described road segment segment, estimate the orientation related with described data sample by using by the reported position of a plurality of data samples indications of the vehicle report of the described data sample of report.

230. method according to claim 228, wherein, one of described one or more road segment segment is a part that is included in the road of the vehicle that travels on two reverse directions, a wherein said road segment segment determines that based on the orientation related with described one or more data samples not representative one or more data samples of described road segment segment comprise at least in part corresponding to the vehicle that travels on the described both direction: determine that its related orientation is not representative to a described road segment segment with the corresponding data sample that travels on another of described both direction.

231. method according to claim 228, wherein, one of described one or more road segment segment is a part that comprises the road in a plurality of tracks with the vehicle that travels on a plurality of directions, a described road segment segment determines that based on the orientation related with described one or more data samples not representative one or more data samples of a described road segment segment comprise: determine that its related orientation is not representative to a described road segment segment corresponding to the data sample of described one or more directions at least in part corresponding to the subclass in a plurality of tracks with the vehicle that travels on described a plurality of directions one or more.

232. method according to claim 228, wherein, one or more other road segment segment with one or more other roads of described one or more road segment segment are overlapping, with on the different one or more directions of one or more other directions of the vehicle that travels on described other road segment segment, and determine not representative the comprising of one or more data samples of a described road segment segment at least in part based on the orientation related in the vehicle ' of travelling on the described road segment segment: determine that its related orientation is not representative to a described road segment segment corresponding to the data sample of described one or more directions of a described road segment segment with described one or more data samples.

233. method according to claim 207, wherein, for one or more each of described at least one road segment segment, with other data sample of described one or more data samples and described road segment segment at least some of determining to comprise of one or more data samples of not representing actual vehicle travel conditions on described road segment segment of described road segment segment are compared.

234. method according to claim 207, wherein, one or more each for described at least one road segment segment, to described road segment segment do not represent one or more data samples of actual vehicle travel conditions on the described road segment segment determine comprise: be identified in the subclass of the actual vehicle travel conditions on the road segment segment interested or uninterested, and determine that whether described one or more data samples are corresponding to the subclass of being discerned.

235. method according to claim 207, each all indicates one or more indications of a plurality of data samples of the reported position of vehicle to it also to comprise reception, at least in part based on the reported position of at least one road segment segment corresponding data sample in related one or more positions separately, in described a plurality of data samples at least some each is related with at least one of described road segment segment.

236. method according to claim 235, wherein, each all has the related orientation of the vehicle of described data sample described a plurality of data sample, with data sample related with road segment segment also at least in part based on the related orientation of the vehicle of the corresponding described data sample in related one or more orientation of described road segment segment.

237. according to the method for claim 236, also be included as at least some each of a plurality of data samples, use by the indicated reported position estimation orientation related of a plurality of data samples with described data sample corresponding to the vehicle of data sample.

238. it is, wherein, that data sample is related with road segment segment also at least in part based on one or more vehicle ' features of the vehicle of described data sample except reported position according to the method for claim 235.

239. method according to claim 235, wherein, data sample is related with road segment segment also at least in part based on will extending one or more distances with the related one or more positions of described road segment segment, and described one or more distances are at least in part based on the accuracy of reported position and determine.

240. according to the method for claim 239, wherein, at data sample will be related with road segment segment one or more preset distances of extending of one or more positions at least in part based on the type in data sample source.

241. method according to claim 207, wherein, one or more each at least some road segment segment, a plurality of associated data samples of described road segment segment also comprise a plurality of data samples, its each all by the traffic sensor report that monitors described road segment segment, and each has all reflected the one or more positions corresponding to described traffic sensor on the described road segment segment.

242. method according to claim 241, also comprise the one or more indications of reception to a plurality of data samples, each data sample is all by a plurality of traffic sensor reports that monitor a plurality of road segment segment, adjust at least some traffic sensors of described a plurality of data samples with these data samples of statistical report, and be at least some each of described a plurality of data samples, at least in part based on by the one or more positions that data sample reflected of coupling, that described data sample is related with in the described road segment segment at least one with each related one or more position of described at least one road segment segment.

243. method according to claim 207, wherein, one or more each at least one road segment segment, the definite of one or more data samples who does not represent at actual vehicle travel conditions on the described road segment segment to described road segment segment also comprises a plurality of data samples that identification is reported by the single portion vehicle that travels on described road segment segment, and based on determining that from the pooling information of a plurality of data samples of being discerned a plurality of data samples that identify are under-represented.

244. method according to claim 207, wherein, one or more each at least one road segment segment, each has also reflected the report time of the vehicle of data sample at its reported position place a plurality of associated data samples of described road segment segment, to the automatic analysis of described a plurality of associated data samples of described road also corresponding to predetermined amount of time, so that the actual vehicle travel conditions on described road segment segment is the travel conditions in the section at the fixed time.

245. method according to claim 207, also comprise, for each of a plurality of different time periods, receive a plurality of associated data samples of one in the described road segment segment, each associated data sample has all reflected the reported position of the vehicle at the report time place on described road segment segment in the described time period, and wherein be used for the automatic analysis of a described road segment segment for each execution of described time period, this is analyzed automatically based on the data sample of its report time in the described time period.

246. method according to claim 207, wherein, for one or more each of at least one road segment segment, carry out determining to one or more data samples of not representing actual vehicle travel conditions on described road segment segment of described road segment segment in real-time basically mode.

247. method according to claim 246, wherein, before by vehicle report data sample, obtain and at least some of at least some related a plurality of data samples of described road the reports that produce at least some data samples in real-time basically mode in the one or more backs that obtaining at least one data sample by the vehicle on described road segment segment of travelling.

248. a computer-readable medium, its content can make computing equipment visit the data sample of representing driving vehicle by carrying out following method:

Receive the indication of a plurality of data samples, each data sample is reflected in one report travelling characteristic in the multi-section vehicle of one or more travels down, and the travelling characteristic that is used for the report of described data sample has reflected the position of vehicle;

On behalf of interested actual vehicle, whether that automatically determines described a plurality of data samples one or morely can not on described one or more roads to travel, and determines based on described travelling characteristic at least in part; With

Provide the one or more indications that are not confirmed as under-represented data sample, so that indicated data sample is used to assist travelling on described one or more roads.

249. computer-readable medium according to claim 248, wherein, the travelling characteristic that each data sample is reported comprises the reported position with the corresponding vehicle of data sample, wherein determine described a plurality of data samples one or more whether can not represent on described one or more roads interested actual vehicle travel and comprise: at least in part based on the reporting vehicle position of not corresponding those data samples of actual vehicle traveling-position on described one or more roads, determine under-represented one or more data samples.

250. computer-readable medium according to claim 249, wherein, at least in part based on determining that with the reporting vehicle position of not corresponding those data samples of actual vehicle traveling-position on described one or more roads under-represented one or more data samples comprise: determine that the reporting vehicle position of described those data samples does not correspond to the interested precalculated position of described one or more roads.

251. computer-readable medium according to claim 248, wherein, the report travelling characteristic of each data sample comprises the report orientation with the corresponding vehicle ' of data sample, determine described data sample one or more whether can not represent on described one or more roads interested actual vehicle travel and comprise: based on the reporting vehicle of those not corresponding data samples of one or more orientation of travelling with the actual vehicle on the described one or more roads orientation of travelling, determine under-represented one or more data samples at least in part.

252. computer-readable medium according to claim 251, wherein, determine that based on the reporting vehicle of those not corresponding data samples of one or more orientation of travelling with the actual vehicle on the described one or more roads orientation of travelling under-represented one or more data samples comprise at least in part; Determine be used for described one or more roads the reporting vehicle of interested those the not corresponding data samples of orientation of being scheduled to the to travel orientation of travelling.

253. computer-readable medium according to claim 248, wherein, determine one or more data samples whether can not represent on described one or more roads interested actual vehicle travel and comprise: based on from each the report travelling characteristic of at least one data sample that is used for these or multi-section vehicle, one or one or more behaviors of multi-section vehicle that identification is added, and be that at least one not representative data sample is determined in uninterested behavior based on the behavior of being discerned at least in part.

254. according to the computer-readable medium of claim 248, wherein, described computer-readable medium is the storer of computing equipment.

255. according to the computer-readable medium of claim 248, wherein, described computer-readable medium is a data transmission media, transmits data-signal that produced, content.

256. according to the computer-readable medium of claim 248, wherein, described content is to make computing equipment carry out the instruction of described method when being performed.

257. a computing system that is configured to estimate the data sample of representing driving vehicle comprises:

First assembly, it is configured to receive indication to a plurality of data samples of road, near the vehicle location each data sample reflection road into each of a plurality of roads; With

The data sample filter assemblies, it is configured to into a plurality of roads at least some,

Automatically determine in a plurality of data samples of described road the vehicle location that it reflected not corresponding on described road one or more data samples of interested traveling state of vehicle; With

Indicated data sample provides one or more indications, so that can be used for assisting travelling on road to a plurality of data samples of the described road except determined data sample.

258. computing system according to claim 257, wherein, for one or more each of a plurality of roads, to the vehicle location that it reflected of described road not corresponding on described road interested traveling state of vehicle data sample determine comprise: determined to reflect not the vehicle location that the precalculated position with road is complementary.

259. computing system according to claim 257, wherein, described data sample filter assemblies also is configured to one or more each at least one a plurality of road, at least in part based on one or more preset bearings of described road, determine automatically described road the one or more vehicle ' orientation that is reflected and with on described road one or more data samples in interested vehicle ' orientation corresponding.

260. according to the computing system of claim 257, wherein, each all is included in the instruction of carrying out in the storer of described computing system described first assembly and described data sample filter assemblies.

261. computing system according to claim 257, wherein, described first assembly comprises receiving trap, be used for each at a plurality of roads, reception is to the indication of a plurality of data samples of described road, each data sample is reflected near the position of the vehicle the described road, and described in the data sample filter assemblies comprise device, it is at least some each of described a plurality of roads, automatically determine in a plurality of data samples of described road the vehicle location that it reflected not corresponding on described road one or more data samples of interested traveling state of vehicle, and one or more indications to a plurality of data samples of the described road except determined data sample are provided, so that indicated data sample can be used for assisting travelling on road.

262. a computer implemented method, it is by providing the reliable data readings of the road traffic sensors related with road in the mode that accurately is reflected in the actual vehicle travel conditions on the road, thereby service road travels, and described method comprises:

Reception is to the indication of a plurality of road segment segment of one or more roads, and each road segment segment has the road traffic sensors of one or more associations, and provides the data of closing the vehicle travel speed by described road traffic sensors; With

In the time period recently, provide reliable Vehicle Speed data by following steps automatically at least some each of described road traffic sensors:

Receive a plurality of data readings from described road traffic sensors, each of described a plurality of data readings all is included in or the travel speed of multi-section vehicle that correlation time, the place was reported by described road traffic sensors in the time period recently;

Based on the data readings that is received, determine that the current data reading of described road traffic sensors distributes the Vehicle Speed of being reported to be reflected in the time period recently;

The averaged historical data readings of determining described road traffic sensors distributes, with be reflected in corresponding one or more previous time periods of time period recently in average Vehicle Speed, a plurality of data readings that described averaged historical data readings distributes and receives from described road traffic sensors based in one or more previous time periods;

Be based upon at least in part the statistical measures of each entropy of determining that current and average historical data reading distributes and definite current and average historical data reading distribute between the statistical measures of similarity, produce the comparison of the current and average historical data reading distribution of described road traffic sensors;

Enough poor between distributing based on the current and average historical data reading of more whether indicating described traffic sensor that is produced at least in part determines whether described road traffic sensors may provide reliable data readings to reflect the possible breakdown of described road traffic sensors in the time period recently; With

Might be able to not be provided at the reliable data readings in the time period recently if determine described road traffic sensors, then not estimate reliable car speed in the time period recently at least a portion of the road segment segment related with described road traffic sensors based on the mode of the data readings that in the time period recently, receives, and provide estimating vehicle speed as the replacement of the interior data readings that receives of time period recently

So that assist travelling on one or more roads by providing the authentic data of closing the vehicle travel conditions.

263. method according to claim 262, also be included as one or more each of at least some described road traffic sensors, in the described time period recently, whether be confirmed as reliable data readings may be provided based on described road traffic sensors at least in part, determine the sensor health status of described road traffic sensors, and the indication to the determined sensor health status of described road traffic sensors is provided.

264. method according to claim 262, wherein, at one or more each of described at least some road traffic sensors, to time period recently of at least a portion of the related road segment segment of described road traffic sensors in the estimation of reliable car speed be based on the reporting vehicle travel speed of second road segment segment relevant with the associated road segment segment of described road traffic sensors, the information of forecasting of the Vehicle Speed that in the time period recently, on the road segment segment related, occurs that reflection is predicted with described road traffic sensors, with in the historical average Vehicle Speed of the road segment segment related at least one with described road traffic sensors.

265. method according to claim 262, wherein, one or more each for described at least some road traffic sensors, also following tolerance is next classifies automatically to possible reliability based on using at least in part to determine whether may be provided at interior reliable data readings of described time period recently by described traffic sensor, described tolerance is: the statistical measures of the entropy of each that the current and average historical data reading of determined described road traffic sensors distributes, and the statistical measures of the similarity between the described current and average historical data reading distribution of determined described road traffic sensors, described automatic classification is carried out by neural network.

266. method according to claim 265, wherein, one or more each for described at least some road traffic sensors, determine whether described traffic sensor may be provided at reliable data readings in the described time period recently also at least in part based on by the mode of operation indication that described road traffic sensors provided, and whether the described road traffic sensors time period formerly may provide reliable data readings.

267. method according to claim 266, wherein, for one or more each of described at least some road traffic sensors, corresponding to one or more time periods the preceding of time period recently comprise a plurality of selections mate all several (day-of-week) related with time period recently and with a plurality of time periods of at least one in (time-of-day) constantly related hour nearest time period.

268. method according to claim 262, wherein, in the radar ranging equipment that each of described at least some road traffic sensors be the movable sensor that is embedded in loop sensor in the road, installs adjacent to road, install adjacent to road, the radio frequency identification equipment installed adjacent to road one, and each of wherein said at least some road traffic sensors is configured to by described road traffic sensors measuring vehicle travel speed.

269. method according to claim 262, wherein, for one or more each of described at least some road traffic sensors, each of at least some of a plurality of data readings that receive from described road traffic sensors also comprises: the reporting quantities of the driving vehicle of being gathered by described road traffic sensors in the time period and/or the indication of the mode of operation of described road traffic sensors.

270. method according to claim 262, wherein, for one or more each of described at least some road traffic sensors, the statistical measures of determined similarity between described current and average historical data reading distributes is based on the Kullback-Leibler divergence between distributing at described current and average historical data reading.

271. method according to claim 262, wherein, the described time period recently is a part of one day, and wherein provide the reliable Vehicle Speed data of one or more each of described at least some road traffic sensors to be carried out repeatedly automatically by every day, so as each of whole day continuous time section reliable Vehicle Speed data readings is provided.

272. a computer-executed method is used for providing from the road traffic sensors on one or more roads the authentic data reading of relevant traffic, described method comprises:

On related road, have one or more road traffic sensors of relative position for each, in the time period, receive the information of a plurality of data readings of gathering by described road traffic sensors, each data readings has correlation time, and reflected the relative position of the related road of described road traffic sensors, at one or more measured values of the traffic at correlation time place; With

At each of described one or more road traffic sensors,

Automatically determine in the described time period whether a plurality of data readings of being gathered by described road traffic sensors may be unreliable, the described small part ground that is determined to compares with information by previous a plurality of other data readings of gathering of described road traffic sensors automatically based at least some the information with these a plurality of data readings;

May be unreliable if interior a plurality of data readings by described road traffic sensors collection of described time period are not confirmed as, indication then is provided, represents the actual traffic situation at relative position place of the related road of described road traffic sensors in the described time period to use these a plurality of data readings; With

May be unreliable if interior a plurality of data readings by described road traffic sensors collection of described time period are confirmed as, indication then is provided automatically, to use other estimated data to replace these a plurality of data readings to represent the actual traffic situation at relative position place of the related road of described road traffic sensors in the described time period, described other estimated data is at least in part based on other road traffic data relevant with these a plurality of data readings

Thereby may insecure road traffic sensors data readings assist travelling on one or more roads by automatic removal.

273. method according to claim 272, also comprise, at at least one each in one or more described road traffic sensors, the sensor health status of determining described road traffic sensors in the described time period at least in part based on: at least some information of these a plurality of data readings is compared automatically with information by previous a plurality of other data readings of gathering of described road traffic sensors, and is provided for the indication of the determined sensor health status of described road traffic sensors.

274. method according to claim 273, wherein, the sensor health status of having determined described road traffic sensors in the described time period be unhealthy after, in one or more follow-up time periods, whether may insecurely determine automatically also at least in part based on determined unhealthy condition in the described time period the data readings of in these follow-up time sections, gathering by described road traffic sensors.

275. method according to claim 272, wherein, at least one each for described one or more road traffic sensors, automatically determine that a plurality of data readings of being gathered by described road traffic sensors whether may unreliable comprising in the described time period: based at least some of a plurality of data readings of described road traffic sensors, the current data reading of determining described road traffic sensors distributes, to reflect the traffic in the described time period, and based on determining that by previous a plurality of other data readings of gathering of described road traffic sensors the averaged historical data readings distributes, to reflect the average traffic in one or more previous time sections.

276. method according to claim 275, wherein, for each of described at least one road traffic sensors, at least some information of these a plurality of data readings compared with information by previous a plurality of other data readings of gathering of described road traffic sensors comprise: the statistical measures that will be used for the information entropy of current and average historical data reading distribution compares.

277. method according to claim 275, wherein, for each of described at least one road traffic sensors, at least some information and compared by the information of previous a plurality of other data readings of gathering of described road traffic sensors of these a plurality of data readings is comprised: the statistical measures of determining similarity between current and average historical data reading distributes.

278. according to the method for claim 277, wherein, for each of described at least one road traffic sensors, the statistical measures of similarity calculated based on the Kullback-Leibler divergence between the current and average historical data reading of determining distributed.

279. method according to claim 272, wherein, for at least one each of described one or more road traffic sensors, the information of at least some a plurality of data readings and information by previous a plurality of other data readings of gathering of described road traffic sensors compared also comprise: the information at least some a plurality of data readings is classified.

280., wherein,, carry out described classification by at least one of neural network, decision tree and Bayes classifier for each of at least one road traffic sensors according to the method for claim 279.

281. method according to claim 272, wherein, for at least one each of described one or more road traffic sensors, other estimated data of being used to replace a plurality of data readings of being gathered by described road traffic sensors in the described time period is also at least in part based on the combination of at least some other the road traffic sensors data readings relevant with these a plurality of data readings.

282. method according to claim 281, wherein, for one of at least one road traffic sensors, described at least some other road traffic sensors data readings comprise: by the data readings of the one or more road traffic sensors collections that close on the related road that is positioned at described road traffic sensors.

283. method according to claim 282, wherein, a described road traffic sensors is in a plurality of traffic sensors that one of a plurality of road segment segment are associated in the related road with a described road traffic sensors, the part that described one or more road traffic sensors of closing on are one of described a plurality of road segment segment.

284. method according to claim 282, wherein, a described road traffic sensors is in a plurality of traffic sensors that one of a plurality of road segment segment are associated in the related road with a described road traffic sensors, and described one or more road traffic sensors of closing on are the parts of one or more other road segment segment adjacent with a described road segment segment.

285. method according to claim 281, wherein, for of at least one road traffic sensors, described at least some other road traffic sensors data readings comprise: in one or more data readings of being gathered by described road traffic sensors in the preceding time period, selectedly one or morely be matched with the time classification related with the described time period at least in part in the preceding time period.

286. method according to claim 281, wherein, for at least one each in one or more road traffic sensors, described at least some other road traffic sensors data readings comprise data sample, and described data sample is from the mobile data source of the related travels down of closing on relative position of inherent described road traffic sensors of described time period.

287. method according to claim 272, wherein, for at least one each in one or more road traffic sensors, other estimated data who is used to replace a plurality of data readings of being gathered by described road traffic sensors in the described time period is also at least in part based on information of forecasting, described information of forecasting has reflected the predicted traffic that will occur at the relative position place of the road related with described road traffic sensors in the described time period, based on the current traffic state data in time place that produces information of forecasting in the described time period, be right after and before the described time period, produce described information of forecasting at least in part.

288. method according to claim 272, wherein, for at least one each in one or more road traffic sensors, other estimated data who is used to replace a plurality of data readings of being gathered by described road traffic sensors in the described time period is also at least in part based on forecast information, described forecast information has reflected the traffic that will be occurred at the relative position place of the road related with described road traffic sensors by forecast in the described time period, before the described time period, produce described forecast information earlier, thereby the current traffic condition data when producing forecast information are not used as the part of the forecast information that produces the described time period.

289. method according to claim 272, also comprise, for of described road traffic sensors, can not receive in the described time period information by at least some data readings of losing of a described road traffic sensors collection, and indication is provided automatically, represents the actual traffic situation at relative position place of the related road of a described road traffic sensors in the described time period with the data readings of using other estimated data to replace to lose.

290. method according to claim 272, also comprise, for at least one each in described one or more road traffic sensors, whether be confirmed as based on a plurality of data readings of being gathered in the described time period by described road traffic sensors at least in part may be unreliable, automatically determine the mode of operation of described road traffic sensors, and the indication of described mode of operation is provided.

291. method according to claim 272, also comprise, for at least one each in described one or more road traffic sensors, determine automatically by a plurality of data readings that described road traffic sensors was gathered in the described time period whether be confirmed as may be unreliable a plurality of based on following content: all several (day-of-week) in the week related also with the described time period, the moment (time-of-day) in one day related with the described time period, the indication of the mode of operation that provides by described road traffic sensors, whether described road traffic sensors may provide reliable data readings in one or more previous time sections, and by the disappearance of the normal data readings of gathering of described road traffic sensors.

292. method according to claim 272, also comprise, for at least one each in described one or more road traffic sensors, each comprises a plurality of data readings of described road traffic sensors: in the report speed of the vehicle ' that the place is gathered by the described road traffic sensors correlation time of described data readings.

293. method according to claim 272, also comprise, for at least one each in described one or more road traffic sensors, each of a plurality of data readings of described road traffic sensors comprises: the indication of the reporting quantities of the vehicle ' of being gathered in the time period by described road traffic sensors and/or the mode of operation of described road traffic sensors.

294. method according to claim 272, also comprise, for at least one each in described one or more road traffic sensors, provide the authentic data reading of described road traffic sensors, described authentic data reading to comprise at least some and/or other estimated data of a plurality of data readings to one or more traffic data clients.

295. according to the method for claim 272, wherein, at least one each comprises in described one or more road traffic sensors: be embedded at least one loop sensor in the related road of described road traffic sensors; Movable sensor with the adjacent installation of related road of described road traffic sensors; Radar ranging equipment with the adjacent installation of related road of described road traffic sensors; And with the radio frequency identification equipment of the adjacent installation of related road of described road traffic sensors, each of at least some road traffic sensors is configured to measure traffic at the relative position place of the related road of described road traffic sensors.

296. according to the method for claim 272, wherein, repeatedly carry out described method every day, so that at least some authentic data of described one or more road traffic sensors is provided for each of a plurality of parts of this day.

297. a computer-readable medium, its content makes computing equipment that the authentic data reading relevant with traffic on the road from road traffic sensors can be provided by carrying out following method, and described method comprises:

A plurality of data readings that reception is produced by the traffic sensor related with road, each data readings has reflected one or more measured values of locating traffic on the related road correlation time;

Compare with information based at least some information at least in part, determine the current reliability of described traffic sensor automatically by previous a plurality of other data readings that produce of described traffic sensor with these a plurality of data readings; With

The indication of the determined current reliability of described traffic sensor is provided, so that travelling on the road, thereby will not be used to represent the actual traffic situation by the data readings that current unreliable traffic sensor produces.

298. computer-readable medium according to claim 297, wherein, the information of at least some of a plurality of data readings comprises: first data readings based on described at least some a plurality of data readings distributes, and relevant information by previous a plurality of other data readings that produce of described traffic sensor comprises: distribute based on second data readings by previous a plurality of other data readings that produce of described traffic sensor.

299. computer-readable medium according to claim 298, wherein, at least some information and compared by the information of previous a plurality of other data readings that produce of described traffic sensor of a plurality of data readings is comprised: determine the statistical measures of the similarity of described first and second data readings between distributing, and each the statistical measures of entropy that is identified for that described first and second data readings distribute.

300. according to the computer-readable medium of claim 297, wherein, the current reliability of described traffic sensor determine also at least in part based on: at least some the information to a plurality of data readings is classified.

Computer-readable medium according to claim 297, wherein, the correlation time of described a plurality of data readings is in current slot, the current reliability of determining described traffic sensor is also determined based on described traffic sensor the automatic of reliability in one or more time periods the preceding at least in part at current slot.

Computer-readable medium according to claim 297, wherein, described method also comprises, if the current reliability of the described traffic sensor of determining is reliable, then provide at least some of described a plurality of data readings, to be used to represent to locate correlation time actual traffic situation on the related road, if the current reliability of the described traffic sensor of determining is unreliable, then provide other estimated data, to be used to represent to locate correlation time actual traffic situation on the related road.

According to the computer-readable medium of claim 297, wherein, described computer-readable medium is the storer of computing equipment and at least one in the data transmission media, and described data are urged function medium transmission data-signal that produced, that comprise described content.

According to the computer-readable medium of claim 297, wherein, described content is to make computing equipment carry out the instruction of described method when it is performed.

A kind of computing equipment of authentic data that is configured to provide from traffic sensor the traffic of relevant related road comprises:

Storer;

First module, be configured in the time period place of a plurality of different times, after the information of one or more measured values of traffic of related road that received reflection that the traffic sensor related with road produce, compare based on information that will be produced and the out of Memory that had before produced by described traffic sensor at least in part, automatically determine institute's generation information reliability aspect the actual traffic situation on the related road in the described time period of expression, described out of Memory has reflected one or more measured values of the traffic on the related roads in one or more sections At All Other Times; With

Second module, be configured to provide to the indication determined of the reliability aspect the actual traffic situation on the related road in the described time period of expression of institute's generation information, so that represent reliably that by using the information of the actual traffic situation on the related road assists travelling on the described related road.

Computing equipment according to claim 305, wherein, automatically determine in the described time period of expression that the reliability of the information that produces of actual traffic situation also comprises on the related road: determine whether institute's generation information has reflected provides the minimum number of enough fiduciary level in the described time period measured value, only at the generation message reflection just carry out the comparison of institute's generation information and out of Memory during the measured value of minimum number.

Computing equipment according to claim 305, wherein, if institute's generation information does not reflect the measured value of minimum number and enough fiduciary levels in the described time period can not be provided, then at least in part based on other road traffic data of the corresponding road of described traffic sensor part, use the data of other estimation to replace the information that is produced, the indication that the reliability of the information that produces that is provided is determined comprises the indication that described other estimated data is provided.

Computing equipment according to claim 305, wherein, the reliability of the information that produces that is provided determines that indication comprises: if institute's generation information is determined is reliable, then provide indication to use the actual vehicle travel conditions of interior road of institute's described time period of the information representation that produces, if it is insecure that the information that is produced is determined, the data that then provide indication to use other estimation are illustrated in the actual vehicle travel conditions of road in the described time period.

According to the computing equipment of claim 305, wherein, described first and second modules are included in the software instruction for execution in the storer.

310. computing equipment according to claim 305, wherein, described first module comprises device, be used for after a plurality of different time place of described time period receives the information of one or more measured values of the traffic on the related road of reflection of the traffic sensor generation related with road, at least in part based on institute's generation information and the out of Memory that had before been produced by described traffic sensor are compared, automatically determine the reliability of the information that produces of actual traffic situation on the interior related road of described time period of expression, described out of Memory has reflected one or more measured values of the traffic on the one or more internal association of section At All Other Times roads, described second module comprises device, is used to provide the indication of determined institute generation information reliability aspect the actual traffic situation on the described time period internal association road of representative.

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