CN102254434B - 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

The present invention is application number be 200780015916.2 the dividing an application of patented claim of (" using the data estimation road traffic conditions from the 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 larger ground of road capacity speed, the traffic congestion of surge is to commercial and government operation and individual happiness generation ill effect.Therefore, carry out in every way the traffic congestion that surge is resisted in various effort, offered individuals and organizations such as the information by obtaining current traffic condition and with information.Can be by variety of way (for example, via radio-frequency (RF) broadcast, internet site, internet site has shown the map of geographic area, wherein current traffic congestion is represented by coloud coding information 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 being sent 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 be in the situation that limited some values that provide, such information usually only limits to a few regions at every turn and usually lacks the enough details that are enough to use.

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 provides.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 out 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 configure or design (is for example reported relevant driver condition, whether their function is normal), even if the status information of having reported the driver also may be incorrect (for example, report driver function normally but in fact really not so), so just very difficultly can not determine maybe whether the data that provided by traffic sensor accurate.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 is the block scheme that illustrates for estimate at least in part the data stream between the assembly of embodiment of system of road traffic condition based on the data of obtaining from vehicle and other Mobile data source.

Fig. 2 A-2E illustrates the example of estimating at least in part road traffic condition 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 (Data Sample 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 revising the data sample that 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 to travel on the comfortable road 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 the data filling from one or more other sources, such as the data that read by the physical sensors that obtains in road annex or road.Based on the data sample that obtains (for example, from road traffic sensors, from each Mobile data source or collect the data that data point reads) can comprise 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 obtaining in certain embodiments by the Mobile data source (for example can comprise, vehicle) a plurality of data samples that provide, from the data readings based on the traffic sensor of road (for example being embedded in the loop sensor in the pavement of road), and from the data of other data source.Data can be with the variety of way analysis of total vehicle total amount of estimating in the specific part such as the average traffic speed of estimation and interested road etc. so that determine interested traffic feature, so that with in real time or be bordering on the mode that (is for example receiving bottom data sample and/or reading) in real time and carry out determining of traffic.For example, the data of obtaining can adjust to detect and/or proofread and correct the mistake in data in every way.If the road traffic condition information of obtaining is coarse interested actual traffic situation feature that maybe can not represent, then in each embodiment, can also filter in every way to remove data, comprise by with at least part of non-interested data sample based on 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 and carries out related with data sample and specified link.Data sample after the filtration (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 obtaining can comprise the traffic (for example, the magnitude of traffic flow and/or average traffic speed) that is identified at least in part road network various piece in the specific geographical area based on the data sample that obtains.Then can carry out with the data of estimating and relate to prediction, forecast, and/or other function of traffic relevant information is provided.In at least some embodiment, the data sample management system is prepared by the employed data of traffic data client with at least some described technology, 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 in every way and/or proofread and correct mistake in the current data (data readings that for example, receives from road traffic sensors).Particularly, such as the analysis based on the data sample that is provided by these data sources, describe to be used for the technology of " health " of the estimation particular source traffic sensor of road (for example based on) so that the specified data source is whether 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, whether significantly different from former common data readings to determine current traffic data reading, 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 to reflect unusual current traffic condition.In each embodiment, can carry out in every way this determination and analysis to possible errors in particular source and/or the current traffic data reading, this will more discuss in detail following, comprise at least part of based on the sorting technique such as use neural network, Bayes classifier, decision tree etc.

Detecting such as behind the corrupt data sample from the damaged data source that does not work, can proofread and correct by this way or revise this corrupt data sample (and missing data sample).For example in certain embodiments, can be by (for example originating to revise one or more data sources with one of relevant information or other, traffic sensor) obliterated data and corrupt data, for example by from closing on or data sample is (for example when the relevant traffic sensor of other normal operation, 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, by the expected data reading of determining one or more data sources with foresight and/or the logical condition information of forecast sexual intercourse of these data sources), via the historical information of one or more data sources (for example, by using historical average according to reading), via with relevant consistent deviation or other type of error that can compensate of leading to errors adjust with the correction data sample etc.Relate to and revise that lose will be in following detailed description with other details of corrupt data sample.

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, a plurality of not existence of unhealthy traffic sensors of normal operation 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; such as the following more detailed description ground of wanting; each interested road can be by coming modeling with a plurality of road segment segment or represent, 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 for particular lane highway section (or other group of a plurality of relevant traffic sensors) in every way, for example by using the traffic related information that is used for estimation adjacent road section, (for example be used for the information of forecasting in particular lane highway section, in the future time section limited such as three hours etc., produce, 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 future time section such as two weeks or longer time, produce, in order to do not use for the current and recent condition information of prediction some or all), the historical long-run average in particular lane highway section etc.By using such technology, even if when only having 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 GPS (" GPS ") equipment and/or other geolocation device that can determine geographic position, speed, direction and/or other sign or relate to the data of Vehicle Driving Cycle, 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), (for example be operated to obtain the vehicle group of such traffic related information, by the predetermined route that travels, or the dynamic direction that changes on road of travelling, to obtain the information about interested road), (for example be mounted with the vehicle of mobile telephone equipment, 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 who travels on computing equipment and the road, be 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 so that the position of equipment and/or mobile message are determined with the 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 identify 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 position and/or the speed of vehicle) etc.

Can use in every way the road traffic condition information that obtains from the Mobile data source, no matter separately or with 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; Etc.) 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 the data source related with vehicle 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, position indication, timestamp and status identifiers.Coming source identifier can be that sign is as numeral or the string of 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 that relates to from the data in Mobile data source minimizes (no matter being permanent or temporary transient related), for example by to stop the mode of identifying the Mobile data source related with this Mobile data source and identifier based on identifier to create and/or the operate source identifier.The speed indication can reflect the instant or average velocity (for example, mph.) in the Mobile data source of in every way expression.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 in every way expression.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 be by coming modeling with a plurality of road segment segment or representing.Each road segment segment can be used for the part of expression 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 specific 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, they can overlapping or any road segment segment all have the part of phase mutual interference.In addition, road segment segment can represent the one or more traveling lanes on the given physics road.Therefore, on each of both direction, 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 at 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 at least in part based on the process flow diagram of 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 occur via various mechanism, comprise direct stream (for example, by realize by parameter 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.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.Data source 101 based on vehicle can be included in a plurality of vehicles that travel on one or more roads, its each miscellaneous equipment that can comprise one or more computing systems and/or can provide pass vehicle running data.Such 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 be installed in each street, highway or other road, near upper or a plurality of sensors, for example be embedded in loop sensor in the road surface and can measure time per unit 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, the 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.

Although the data source 101-103 in this example directly offers data sample each assembly 104-108 and 110 of data sample management system 100, data sample also can be processed first before being provided for these assemblies in other embodiments.Such processing can comprise identity (for example, vehicle, the traffic sensor etc.) tissue in time-based, position, geographic area and/or individual data source 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 obtains from the road traffic sensors of a plurality of geographically colocated 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 specific vehicle was provided At All Other Times.Can also further process 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 alternatively data sample stream estimation assembly 108 data sample that obtains is filtered.As will more discussing ground in detail elsewhere, such filtration can comprise: with related corresponding to the road segment segment of road in the geographic area, and/or identification is not corresponding to interested road segment segment or reflect the data sample of uninterested vehicle location or behavior with data sample.Can comprise data sample is related with road segment segment: reported position and/or orientation with each data sample 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 interested road segment segment: remove or identify such data sample so as not to their modelings, consider or processed by other assembly 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 corresponding to the part of uninterested road or zone (for example, ramp and collector/distribution lane/tell highway road) etc.Whether the recognition data sample reflects that uninterested vehicle location or behavior can comprise: identify and (for example be in idle condition, engine is leaving and is 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 identify road segment segment be that (or not being) is interested.For example, such filtration can comprise at special time period (for example to be analyzed, 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, or their functional category of roads unavailable for the sensing data reading represents less or the road segment segment of travel still less) as uninterested road and road segment segment in order to from further analysis, get rid of.

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 processed 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 discuss ground in detail as following, data after then will proofreading and correct offer sensor collection assembly 110 (also offer alternatively other assembly, 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, to be compared with within the corresponding time period (for example, identical week fate in one day identical time), being distributed by the history of the data sample of this road traffic sensors report by the distribution of the current data sample of given road traffic sensors report.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 detect with such indication the mistake of the data sample that obtains.If in the data sample that obtains, detect mistake, then can revise in every way the data sample of makeing mistakes, comprise be used to from determine error-free adjacent/mean value of the data sample on adjacent (for example, the next door) of 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 is provided by predictive traffic information systems by using, revise the data sample of makeing mistakes.Other details that relating to predicted traffic information provides will provide in addition.

The data sample exceptional value is removed the data sample after assembly 106 obtains to filter from data sample filter assemblies 104 and/or is adjusted assembly 105 from sensing data and obtains to adjust or revised data samples, and then identification and considering is removed those and do not represented the data sample that interested road and the actual vehicle on the road segment segment are travelled.In an illustrated embodiment, for each interested road segment segment, block analysis is recorded 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 like this to the determining of non-representative data sample, 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 sample, so that the data sample that obtains in the embodiment shown is illustrated in interested road and the actual vehicle on the road segment segment is travelled.Data sample velocity estimation assembly 107 is then analyzed the data that obtain, with based on this road segment segment (for example, by data sample filter assemblies 104, or by the next reading of sensor from the road segment segment part) the data sample group related with the time period, one or more speed of estimation interested road segment segment within least one interested time period.In certain embodiments, the speed of estimating can comprise that this organizes the speed average of a plurality of data samples, also can (for example, the age (age) is in order to give the newer larger weighting of data sample by one or more attribute weights of data sample; And/or the source of data sample or type, in order to come to the larger 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, 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. vehicle average or the total amount of per unit distance) and estimation occupation due to communication rate (for example, be expressed as taking specified point or regional average or total time quantum such as per minute or the special time amount vehicle that per hour waits) etc.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 adjusted 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 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, so that the estimation traffic that is provided by the assembly such as data sample velocity estimation assembly 107 and/or data sample stream estimation assembly 108 etc. to be provided, 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 in the situation of accurate estimation road traffic condition information alternatively to use.

In an illustrated embodiment one or more traffic data clients 109 estimation that provided by data sample velocity estimation assembly 107 and/or data sample stream estimation assembly 108 is provided road traffic condition information (for example, speed and/or flow data), and can use in every way such data.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, foresight traffic information providing system for example is created in the traffic related information of the future transportation situation forecast of a plurality of future times with traffic related information; 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 that is 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 in the situation of data 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 the data from data sample velocity estimation assembly and/or data sample stream estimation assembly, or outside this, additionally obtain.

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 present invention's exemplary details of being not limited to provide.

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 for having 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 highway or the highway that crosses, be divided in the west to east orientation 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 every way the road shown in Fig. 2 A, 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 larger 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 the first highway section corresponding to track 202b1 the track group 202b in these tracks of enjoying similar traffic feature, 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, only have 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 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 be come to determine at least in part at least some embodiment, for example (for example, physical dimension and/or orientation and/or traffic relevant information (for example, speed limit) related with geography information.

Fig. 2 A also described specified time interval or At All Other Times the section (for example, 1 minute, 5 minutes, 10 minutes, 15 minutes etc.) during a plurality of data sample 205a-k that a plurality of Mobile datas source in 200 (for example, vehicle, not shown) reports that travel in the zone.By one of a plurality of Mobile datas source report the time, each of data sample 205a-k 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 in order to 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 from the physical location of vehicle when the record data sample (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 the more data sample of data sample, if for example sample 205i and sample 205h are by (for example being reported along single section vehicle that road 202 east orientations travel within the time period, by comprising the single transmission for a plurality of data samples of 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 data sample management system can be filtered the data sample that obtains in certain embodiments, in order to data sample is 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 at the preset distance in the road corresponding with road segment segment and/or track (for example, 5 meters) in, and its orientation is in the predetermined angular (for example plus or minus 15 degree) in the orientation in the road corresponding with this road segment segment and/or track, and then data sample is related with road segment segment.Although can be used for carrying out before the data sample management system at data sample to the association of the data sample of road segment segment 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 associated road 203.In certain embodiments, when being illustrated in a plurality of track of travelling on the opposite direction with the single road section, can whether can be related with this road segment segment with determining data sample with the orientation of data sample and two aspect ratios of road segment segment.For example, data sample 205k has roughly opposite with data sample 205a orientation, if but represent two opposite carriageway of road 203 with road segment segment, then it also can be with related corresponding to the road segment segment of road 203.

Yet, because road 203 approaches with track group 202a, also possibly, 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, the 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 large 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 than the speed of observation or the road 203 that sends, its rather than road 203 related with track group 202a just then.Also can be used as a part (for example, the position in this removal like the info class of other type; The orientation; State; Other relates to the information of data sample, such as 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, with track group 202a) reported position, but its orientation (roughly heading west) can be used for 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.

Or this example, data sample 205d can be not related with any road segment segment, because its orientation (roughly eastbound) and corresponding to the reverse direction that is in of 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 report enough closely (for example, in predetermined distance), if it is too far away for example to have the track group 202b in similar orientation, then during filtering from the follow-up use of the analysis of this data sample, get rid 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 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; report speed that can the usage data sample is come (for example to distribute by the expection of the speed that the data sample that is used for each such candidate track is observed (or the magnitude of traffic flow other measure); usually or Gaussian distribution) modeling, and data sample conformed to specific track or mate.For example, because observation, deduction or the historical average speeds of the vehicle that the speed reported of this data sample is travelled closer to HOV track 202a2 than observation, deduction or the historical average speeds of the vehicle that travels at 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 is provided 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 the position that they are reported and orientation are corresponding to 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 is provided 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 relatively high (for example, 30 meters) predeterminable range, and the predeterminable range of low (for example, 1 meter) can comparatively speaking be provided by the data sample that usage variance is proofreaied and correct the Mobile data source of GPS equipment and provided.

In addition, data sample filters the data sample that can comprise the data sample that identification is not corresponding with interested road segment segment and/or can not represent the actual vehicle of travelling at road.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 again Fig. 2 A, 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 Function Classification and not considered by the data sample management system, perhaps also can filtering data sample 205j, do not separate with the expressway because onramp is too short.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 that all represent same position that single Mobile data source provides this Mobile data source has stopped.If other data sample of all related with the same link section all represents mobile Mobile data source, 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 can 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 stop is to send), if so, can come with such indication 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 (namely 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.The report speed of given data sample and writing time can be passed through its location positioning 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 At All Other Times unit) with respect to some starting points.Such as the following more detailed description ground wanted, some embodiment can shown in analyze in the special time window in the time period or process the data that obtain, for example time window 213.In this example, time window 213 comprises recorded data sample within 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 because for example so that traffic to stop-traffic control signal that walking modes flows, 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 relatively high 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 processed, so that the processing degree of accuracy that generation improves or resolution are (for example, by calculating the average velocity reflect more accurately each magnitude of traffic flow speed) and interested additional information is (for example, the speed of difference between HOV traffic and non-HOV traffic), or recognition data sample group is got rid of (for example, not comprising that the HOV traffic is as the part of subsequent analysis).Although do not illustrate here, this different group of data sample can be identified 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 that the data sample that is not illustrated in the up vehicle of sailing in particular lane highway section is got rid of is removed or considered to filtrator executing data sample exceptional value, it is based on the report speed that is used for data sample (although 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 reporting) in this example.Particularly, Fig. 2 C has shown table 220, and it illustrates for the example set executing data sample exceptional value of ten data samples and removes (quantity that is performed in actual use, the data sample of analysis can be larger).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), occur, or alternatively can comprise the subset (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, come the velocity deviation of each speed sample in the determining data sample group by the average velocity of other data sample from group, non-representational data sample is identified as statistics exceptional value with respect to other data sample.Can measure the deviation of each speed sample, the numerical value of the standard deviation that for example differs with respect to the average velocity of other data sample in group, the large 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.Every row 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 being got rid of determine this result's difference from other sample of this group.The data sample of row 223a can be referenced as the first data sample, and the data sample of row 223b can be referenced as the second data sample etc.Row 221b comprises the report speed of each data sample, and it is per hour measured with how many miles.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 larger than 1.5 standard deviations for this example purpose based on the deviation of listing in row 221e, whether row 221f indicates the data-oriented sample 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 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 within 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 processing 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; Be 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, efficiently calculating mean value and standard deviation, 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 the usage data sample is carried out the average velocity estimation, and has shown to be similar to be used for particular lane highway section and the instance data sample of time period 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 interval) of 10 minutes or 15 minutes a plurality of data samples of consideration.If calculate average velocity at such time window, for example at the terminal of time window or be bordering on the end, then when collecting the speed of data sample, the in every way weighting of data sample in time window, (for example for example consider " age " of data sample, 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 than other data source or data source type or particular source than the better data of other data source can be provided, 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 example has been described 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 calculates of 235b.Time window 235a is included in the constantly data sample of record between 30 and 45, and time window 235b is included in the constantly data sample of record between 35 and 50. Data sample 231a and 231b drop in time window 235a and the 235b.

Shown in example in, each data sample and the proportional weighting of its age in the preset time window.That is to say, 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 data-oriented 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).The sample weights of rear (for example, more the time to approach window is terminal) record is greater than the sample that records morning (for example, more the time to approach window begins) in time in time.The weight of data-oriented sample can be found out to it and the place corresponding to the weight map curve intersection of interested time window by paint perpendicular line downwards from data sample on curve 230.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 can find out that similarly the weight 234a of data sample 231a is less than the weight 234b of data sample 231b.In addition, clearly, for the follow-up time window, the weight of data-oriented 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, relatively upgrading.

More normally, in one embodiment, can followingly represent for the 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 constantly T place end 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 calculates 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 for the average velocity that calculates or produce similarly in other embodiments the value of the confidence of other form.

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 simultaneously different weighting function (for example, the weight of data sample is with linear the minimizing rather than exponentially minimizing of age) to carry out time weight.In addition, the data sample weighting can also be based on the sum of the data sample within the interested time interval.For example, above-mentioned variable parameter α can depend on or the sum of based on data sample and changing, so that the quantity of data sample more at most older data sample just (for example produces higher 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 the 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 in 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 occupation due to communication rate can be expressed as vehicle and take 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.From total volume of traffic of inferring, and the average velocity of the estimation of 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, the total amount of then 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 is on the road segment segment of length l, in given time period τ, infer the main average rate (underlying means rate) that the Mobile data source arrives, λ with Bayesian statistics.Can stochastic modeling in Mobile data source that one section road corresponding to road segment segment arrives, 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 observes.For example, assumed average arrival rate λ=10 (vehicle/unit interval) and observation n=5 section vehicle, then replace generation:

$p\left(n\right|\mathrm{\&lambda;})=\frac{{10}^5{\mathrm{<figure-callout\; id="10"\; label="e"\; filenames="HDA0000080817750000031.png,HDA0000080817750000041.png"\; state="\{\{state\}\}">e</figure-callout>}}^{10}}{5!}\&ap;0.038$

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

Above formula can make to determine with Bayes' theorem the possibility of the specific arrival rate λ of the given n of observation.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 with upper, 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 l of road segment segment at time τ within 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 at y axle 241, has indicated the traffic arrival rate amount of inferring at x axle 242, and has indicated the possibility of the traffic value of each deduction at 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 close to total actual traffic amount.

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

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, the description of for example doing with reference to figure 2D.

Figure 10 A-10B illustrates the example such as the misdata sample of unreliable and obliterated data sample etc. of adjusting or revising from road traffic sensors.Particularly, Figure 10 A has shown the Multi-instance data readings that obtains 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.Although 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-004i, it has shown a plurality of data readings that 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 respectively the car speed that sensor 123 arrives at four different time observations, 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.Increasing progressively counting and can be when sensor gathers formerly 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 respectively 316 cars and 389 cars.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 can record in certain embodiments additional traffic sensor data readings and/or additional information can be provided and/or it is recorded as the part of each data readings.Similarly, in certain embodiments, information is lacking of showing than using here described technology.

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 detect unsound traffic sensor with statistics and/or other technology based on the data readings value of reporting.

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 processing 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 distributes based on the data readings of the data readings that obtains from traffic sensor 123 within the interested time period.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 interval, if but within 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, although 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 average velocity other to measure.For example, at least some embodiment, can use similarly the volume of traffic and/or occupancy.

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 expressions are gathered by sensor 123 between the 9:00AM to 12:29PM of specific Monday when sensor 123 functions are normal.Can clearly find out, the shape of histogram 1030 and histogram 1020 are similar, suppose in the expection of the travel pattern of specific Monday similarly with the travel pattern of general Monday, then will discuss as following, can calculate in every way similar degree.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 on the contrary the data readings that can not reflect the actual traffic flow.As obviously find out ground, the shape of histogram 1040 is different from 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, although can use Kullback-Leibler divergence (divergence) between two traffic sensor data distribute to determine similarity between two distributions, 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 represent:

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

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, distribute and the Kullback-Leibler divergence (" DKL ") 1036 that is used for healthy traffic sensor between the data readings shown in the histogram 1030 distributes be about 0.076 in the data readings shown in the histogram 1020, and the data readings shown in the histogram 1020 distribute and between the data readings shown in the histogram 1040 distributes the Kullback-Leibler divergence 1046 for unsound traffic sensor be about 0.568.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 its function traffic sensor 123 when normal) 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 from the Kullback-Leibler divergence and measure or in addition, some embodiment can measure to detect the misdata reading that is provided by traffic sensor, for example statistical information entropy with other statistics.The statistical entropy of probability distribution is the measuring of otherness of probability distribution.The statistical entropy of probability distribution P can followingly represent:

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

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 larger than statistical entropy 1032 and statistical entropy 1022, and this has reflected that traffic sensor 123 has been showed more chaotic output mode when its fault.

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

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 the 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 the 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 large (in this situation large twice), and this has reflected the statistical entropy of the distribution shown in the histogram 1040 and has wanted large 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 measure to detect unsound traffic sensor with above-mentioned statistics in every way.In certain embodiments, the various information that relevant current data reading distributes can be provided as the input to healthy (or the data readings reliability) sorter of sensor, such as based on neural network, Bayes classifier, decision tree etc.For example, the sorter input message can comprise, for example, and the 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 the statistical entropy that distributes of current data reading.Then, sorter is provided based on the input that provides by the health of this traffic sensor, 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, if and/or the entropy difference measure between the 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 such as previous institute, although 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 the embodiment by executing 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 connection 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 preparing to estimate the road traffic condition of each bar road segment segment, for example the 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, in at least some embodiment, the data sample management system with basic in real time or the mode during approximate real carry out the estimation of road traffic condition, for example within a few minutes, obtain bottom data (himself can obtain in substantially real-time mode from data source).

Other traffic information system 360-363 and 369 and/or third party's computing system 390 then can use in every way the data that provided by the data sample management system.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 at a plurality of future times, and information of forecasting offered one or more other receiving ends, one or more other traffic information systems for example, 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 from the data sample management system information of the relevant road traffic condition of estimating, 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 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 (such as what reported by the 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) from the take over party of the next data of RT information providing system.

Client device 382 can adopt various forms in each embodiment, and usually can comprise any communication facilities and other request of producing to traffic information system and/or receive any computing equipment of 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) to the request of the information that relates to traffic (for example to produce, 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, near upper or sensor.These sensors can comprise loop sensor, can measure the speed of the quantity of 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, although 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 to obtain raw data and make data is available (no matter be original form or after it is processed) to traffic information system.

Other data source 388 comprises polytype other data source, and it can be made to provide to user, consumer and/or other computing system by one or more traffic information systems the information of relevant traffic.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, 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 event of impact 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, road job information, the holiday that are provided by manual operation person (for example, the first present members, law enfrocement official, expressway employee, news media, tourist etc.) etc.

In this embodiment each can be to be positioned at that vehicle offers one or more traffic information systems with data and/or from computing system and/or the communication system of one or more these system's receive datas 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 Driving Cycle, 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 the use of vehicle driver.For example, vehicle can comprise Web browser with installation or embedded panel board (in-dash) navigational system of other controlling application program, the user can come from one of traffic information system (for example predicted traffic information provides system and/or RT information providing system) request traffic relevant information with this system, 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 communication 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 and in some way operator's operation of the side's etc. of usage data other people rather than traffic information system.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 receives also is based on the information that receives) offered user or other people (for example, by Web entrance 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 to the consumer, or provide the information of relevant traffic to be used as the Online Map company of an itinerary service part for their user.

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 repeatedly produce a series of future times forecasts, 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 observing of these geographic areas.The future transportation condition information of predictability can use to help travelling or other purpose in every way, so that based on the prediction plan of the traffic of the road of a plurality of future 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 automatically determine 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 in the road segment segment of given road network and interested out of Memory indication (for example, 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 automatically identify interested road, 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 in the daytime (inter-day) of the volume of traffic or other flow changes, and/or the in the daytime variation of congestion in road.Such factor can be analyzed by for example primary clustering (principal component), for example by at first calculating the covariance matrix S that in given geographic area, is used for the traffic related information of all roads (or road segment segment), then calculate the eigen decomposition of covariance matrix S.Then in the descending of eigenwert, the eigenvector of S represents that independently the variation to the traffic of observing 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 by the RT information providing system, perhaps alternatively be provided by one or more other programs 369.Information providing system can use by data sample management system 350 and/or other assembly (providing system 360 such as predicted traffic information) and analyze and the data that provide are come for operation or used client device 382, provide traffic-information service based on 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 with other unshowned equipment connection, comprise 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, the combination in any that can comprise the mutual hardware and software with carrying out described type of functionality of energy, 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 although 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 carry out in can the storer on another equipment 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 produces 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, in order to receive the data sample corresponding to road in the geographic area, and filter out uninterested data sample for the estimation of back.The data sample that filters then can use in every way subsequently, for example calculates the average velocity in interested particular lane highway section and calculates other about the feature of the magnitude of traffic flow for such road segment segment with the data sample that filters.

Routine is the geographic area receive data sample group of special time period 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 interested information (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, although 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 for 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 with this data sample separately.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 for closing on but incoherent road), then such analyzing adjuncts can use the out of Memory such as speed and direction to affect 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 this data sample related with suitable road (for example, by determining to come from the frontage road with 25 mph. speed limits with the data sample of the velocity correlation of 70 mph.s).In addition, in the different road segment segment of the certain extension of road or other road part and many (for example, road for two-way traffic, travelling and be modeled as the first highway section and travelling on another direction is modeled as different second highway section 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) in the situation about being associated, can use the additional information of the sample such as the relevant data such as speed and/or direction to select the road segment segment of most possible road for this data sample.

After step 415, routine proceeds to step 420 and thinks that follow-up processing filters out not related with interested road segment segment any data sample, comprises not related with any road segment segment data sample (if there is).For example, the part of specified link or road may not be that subsequent analysis is interested, (for example for example get rid of the road of specific function road class, if the size of road and/or the volume of traffic be not large enough to can have interested), or owing to the expressway that can not reflect such as such road traffic characteristic partly such as ramp, expressway or special-purpose road or the cross road that crosses/divide as a whole, therefore get rid of such road part.Similarly, in many road segment segment situation related with the specific part on road, some road segment segment are may some purpose interested, if for example only having the behavior in non-HOV track is that specific purpose is interested, if or to only have a direction be interested in the track of both direction, then is that the HOV track is got rid of in the expressway.Although after step 420, routine proceeds to step 425 to determine whether the behavior filtering data sample in based on data source, in other embodiments such 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 corresponding to its behavior can not reflect the interested magnitude of traffic flow behavior of wanting measured data source 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 within the time period that prolongs etc.).After step 430, if or determine in step 425 that alternatively the behavior in based on data source is not filtered, then routine proceeds to the data that step 490 is thought follow-up use stored filter, but the data replacement ground that filters in other embodiments 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 for road segment segment and one group of data sample of time period therein in step 505 beginning.The data sample that receives can be, the data sample of the filtration that for example 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 the 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 within 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 in every way such grouping, for example data sample be fitted to many curves, every curve represents 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 having the expressway in HOV track and other non-HOV track) if for example alternatively cut apart road segment segment for use in all data samples of this road segment segment.

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 (1eave-one-out) analysis in order to select the target data sample that specifically will temporarily be removed and determine average traffic feature for remaining traffic feature.Larger in the average traffic feature that is used for the remaining data sample and the difference that is used between the average traffic feature of all data samples from step 515, then removed target data sample is to reflect that the possibility of exceptional value of public characteristic of other remaining data sample is just larger.In step 525, routine is then carried out the outlier detection of one or more addition type alternatively, thereby thereby the group of removing continuously two or more target data samples is estimated their joint effect, but also can not carry out so additional outlier detection 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 within 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 within the time period, but call alternatively can be for the single time interval (for example, estimating a plurality of time intervals via a plurality of routine call) for 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 within 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 within 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 receives can be, for example, obtains from the output of data sample exceptional value remover routine.Similarly, can obtain from data sample exceptional value remover routine the indication of inadequate data.In some cases, the indication of inadequate data can be based on having data sample in shortage, wrong (for example, adjusting assembly 105 by the sensing data of Fig. 1) when within the time period, not coming data sample from the Mobile data source related with road segment segment and/or losing or be detected as when some or all data readings of road segment segment for example.In this example, routine has continued to determine whether to receive the inadequate indication of data in step 610.If so, 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 within this time interval, and based on one or more factors to the data sample weighting.For example, in an illustrated embodiment, to stand-by period of the weighting based on data sample of each data sample and (for example change, with linearity, index, or step-by-step movement mode), for example give near the larger 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, no matter the in an illustrated embodiment further source of based on data and weighting of data sample for example lays 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 the data readings weighting from another physical sensors the data readings weighting from a physical sensors, thereby (for example reflect the available information of relevant sensor, 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 in certain embodiments comprises the value of the confidence or other estimation of possible errors in the particular data sample, and particular data should the degree related with particular lane highway section etc.

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 afterwards obtainable additional data sample of inherent step 605 reception of time period information subsequently alternatively.In step 645, then determine whether within the time period, will calculate the more time interval, and if like this, then routine turns back to step 625.If alternatively there is not the more time 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, want estimated traffic characteristic be included in the vehicle total amount (or other Mobile data source) that arrives or exist on the time period inherent particular lane highway section, and within 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 within 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, wrong (for example, adjusting assembly 105 by the sensing data of Fig. 1) when within the time period, not coming data sample maybe to lose or be detected as when some or all sensing data readings that are used for road segment segment from the Mobile data source related with road segment segment for example.Routine then continues to determine whether to have received not enough data indication in step 706.If so, 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 total amount and the occupancy of the estimation of road segment segment within the time period.In step 755, routine then provides the indication of 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 on probability part determine to provide the most possible arrival rate of road segment segment of the vehicle of this data sample based on determined vehicle number.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 the determined quantity of vehicle and the relevant vehicle that data sample is provided is inferred within 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 within the time period based on the total amount of inferring, average velocity and average Vehicle length.Also can estimate similarly in other embodiments the magnitude of traffic flow feature of other interested type.In an illustrated embodiment, routine then proceeds to step 790 with the indication of 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, based on the traffic sensor reading that within the indicated time period, recently obtains, carry out this routine to determine the health of one or more traffic sensors each time of one day.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, such as 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 with various intervals level (for example, the 5mph bucket that is used for the data group of velocity information), and in certain embodiments this routine can be take each (or other combined horizontal) of the interval level of one or more each that measure for one or more traffics for specific traffic sensor analysis data.

This routine begins in step 1105, and receive one or more traffic sensors and selected time classification (nearest time classification 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 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 in every way specific chronological classification, 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 rare between the lights, then they are formed one group together.In addition, in certain embodiments, can determine to have by the analysis of history data time period of similar magnitude of traffic flow feature, thereby no matter distinguish different traffic sensor (for example, by geographic area, road, single-sensor etc.) with artificial or automatic mode select time classification.

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 in step 1105, then be the next one combination of traffic sensor and indicated time classification).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 determines that target traffic sensor data distribute.In step 1125, routine is then determined the similarity that target traffic sensor data readings distributes and historical traffic sensor data readings distributes.Such as the other places more detailed description, in certain embodiments, can determine by the Kullback-Leibler divergence of calculating between the 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 discussing elsewhere ground in detail, 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 group of velocity information (for example, for) of the interval degree of measured data.In one embodiment, can classify with neural network, 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 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 the selected related covering of time classification tool is the moment at the hour classification of large time period (for example at least 12 or 24 hours) enough, 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, thereby the health to traffic sensor is classified within the long time period (for example, one day).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 to changing the same day significantly in data readings 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 within the relatively short time period (for example, one to two hour).If the sensor health status is definite only based on the data readings that mainly obtains 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 from (for example, the 12 or 24 hours) data readings that obtains of relatively large time period, can reduce so wrong negative definite risk.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 first chronological classification of the moment at the hour classification of first three hour to have, routine of execution in per three hours) and with the time classification that reflects the whole previous date (for example carry out at least a routine 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 is provided 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 sensors (or combination of traffic sensor and time classification) to process.If so, 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, come periodically (for example, once a day for each each of one or more time classifications that is used for a plurality of traffic sensors, inferior on every Mondays) repeatedly calculate historical data reading distribute (for example, at least 120 days).Distribute by periodically repeatedly calculating the 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) to proofread and correct the data readings that is used for being identified as by sensing data error in reading corrector routine unsound traffic sensor.In other embodiments, can carry out as required this routine, for example by sensor data collection device routine, obtaining to be used for the data readings after the correction in particular lane highway section, or alternatively in various environment, can not be used.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 come analysis and the correction of executing 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 alternatively the indication of processed one or more time classifications (for example, wherein be classified as at least one of the traffic sensor of association be at least potentially unsound time classification).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 processes unsound traffic sensor on indicated road segment segment, the data readings after to determine as 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 etc. and selected the time classification that will use, one or more time classifications indicated in step 1205 for example.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 the healthy traffic sensor of road segment segment that is used for selected time classification.Can determine in every way the correction data reading, 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 in other embodiments selected healthy traffic sensor.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 come averaging with all healthy traffic sensors, 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 for road segment segment and/or unhealthy traffic sensor, the forecast traffic related information that is used for road segment segment and/or unhealthy traffic sensor, and/or for the average traffic related information of the history 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, as long as can obtain) can have precedence over the forecast traffic related information, use the forecast traffic related information can have precedence over again historical average traffic related information.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 at least predetermined percentage 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 the follow-up use of other assembly (for example, the sensor data collection assembly 110 of Fig. 1) (for example, being stored in database or the file system).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 a plurality of traffic sensors related with the particular lane highway section) 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 can collect information from a plurality of traffic sensor groups of other type in other embodiments.In addition, this routine can provide by other routine of the estimation of carrying out traffic related information (for example replenishes, the traffic related information of the information that data sample flow estimation device routine) provides, thus in the situation that other routine can not provide accurate estimation (for example because data deficiencies) that traffic related information is provided.

This routine is in step 1305 beginning and receive one or more section and one or more time classifications or the At All Other Times indication of section.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 available traffic sensor data readings that the routine acquisition is gathered within the indicated time period by all traffic sensors related with this road.Such information for example can be adjusted from the sensing data of Fig. 1 sensing data adjustment assembly 353 acquisitions of assembly 105 and/or Fig. 3.Particularly, routine can be to be confirmed as healthy traffic sensor and obtains the traffic sensor data readings and/or obtain the traffic sensor data readings of proofreading and correct from being confirmed as unsound 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 within 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 the data readings of reporting vehicle quantity.For example, given report is activated from sensor and begins loop sensor by the vehicle cumulative amount of sensor, and then the volume of traffic can be by deducting two data readings obtaining and being removed the result and inferred simply by the time interval between data readings within the indicated time period.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, in every way weighting of data readings (for example, passing through the age) is so that nearer data readings has the impact larger than older 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 process.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 in every way estimation is used for various types of traffic flow informations of road segment segment.In this exemplary embodiment, for example can not obtain in the situation of enough data for accurately carrying out their estimations separately when these routines, routine can estimate that with the estimation of acquisition average velocity and/or by the data sample flow of Fig. 7 the device routine call is with the estimation of acquisition amount and/or occupancy 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 section At All Other Times, and the indication of the traffic flow information of one or more one or more types such as speed, amount, density, occupancy.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, whether has precise information for the traffic flow information of one or more types in the time period shown in one or more based on such road segment segment.Relevant road segment segment can be identified 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 the first highway section usually has and (for example is similar to second, adjacent) the travel pattern of road segment segment, thereby the traffic flow information that is used for the second highway section can be used for estimating the magnitude of traffic flow on the first highway section.In some cases, no matter analysis is in advance and/or Dynamic Execution, can automatically determine such relation, for example based on the section of two road separately magnitude of traffic flow pattern statistical study (for example, be similar to and previously discussedly distribute in the similar data of different time about identifying given traffic sensor, but alternatively analyze in two or more different sensors such as the similarity between the same time).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 based on the value of the same type traffic flow information estimation that is used for one or more relevant road segment segment for the traffic flow information of indicated type.For example, the average velocity of determining this road segment segment based on the average traffic speed of one or more adjacent road sections (for example, by using the traffic speed that comes from an adjacent road section, or the traffic speed averaging to coming from two or more adjacent road sections).

If alternatively in step 1410, determine not to be used for based on relevant road segment segment estimation the traffic flow information of indicated road segment segment, then routine proceed to step 1420 and determine whether within 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 in specific situation, obtains simultaneously accurate current data if for example repeat prediction (for example for ensuing 3 hours per 15 minutes once) for a plurality of following moment.Similarly, if (for example, above three hours) is available for generation of the accurate input data of prediction within time expand, then can need not to obtain the 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 not using 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 Time Series 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 definite 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 being used for this road segment segment and the forecast information of time period within the time period of one or more indications.In certain embodiments, can be for exceeding the future 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, owing to having surpassed three hours with regard to non-availability for generation of the accurate input data of prediction), then still can use forecast information, the information that for example obviously produces in advance.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 based on forecast information in step 1430, 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 for indicated road segment segment.Relate in the U.S. Patent application (application attorney docket is 480234.410P1) that is entitled as " Generating Repre sentative Road Traffic Flow Information 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 provides 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 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 docking (docking) or other tie point physics download (for example, in case the main base of return or other purpose with suitable equipment of can execution information downloading just from fleet's Download Info).Although 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 subsequently institute's observed case have been improved prediction processing etc.), it for example can be the situation from equipment physics Download Info, when with in real time or when being bordering on real-time mode and obtaining, such road traffic condition information provides additional benefit.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 storage and/or transmission size etc. when fetched data).In certain embodiments, this frequently radio communication of the road traffic condition information of obtaining can also (for example replenished by the additional road traffic condition information that obtains At All Other Times, download from the continuous physical of equipment, via few frequency (less-frequency) radio communication that comprises the greater amount data), for example comprise the additional data corresponding to each data point, comprise the collection information of relevant a plurality of data points etc.

Although by from mobile device with in real time or other frequently the mode road traffic condition information that obtains to be obtained various benefits are provided, the radio communication of such road traffic condition information that obtains can retrain in every way in certain embodiments.For example, in some cases, with few frequency interval (for example can be from mobile device via the cost structure of specific radio link (for example, satellite is uploaded) the transmission of data, per 15 minutes) transmission that occurs, 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 lacking wireless coverage in the zone at mobile device place, because the cellular radio receiver base station of not closing on), because other action of being carried 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 so that 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, each data sample of the common individual transmission of mobile device, for example per 30 seconds or 1 minute) time the cycle memory storage road traffic condition information data sample that obtains, and then at the time durations of the next wireless transmission of appearance these data samples of storing are transmitted together.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 the data sample of then during next wireless transmission these being stored similarly transmits (or the subset of these samples and/or set) together.Such as an embodiment, if nearly the wireless transmission cost of 1000 information units is that the size of $ 0.25 and each data sample is 50 units, then per minute sampling and transmission in per 20 minutes comprise that the data group (rather than per minute sends each sample individually) of 20 samples is very helpful.In such embodiments, although 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), the road traffic condition information that then 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 has 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.Although only shown the road of limited quantity, they can represent vast geographic area, for example across several miles interconnective expressways, or have striden the subset of the avenue in several districts.In this example, the Mobile data source (for example, vehicle, not shown) within 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 begins to travel, it is in position 945a acquisition and transmit first data sample (as using shown in the asterisk " ★ " in this example), obtain and transmit second data sample at position 945b after 15 minutes, and altogether obtaining and transmit the 3rd data sample at position 945c after 30 minutes.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, transmission such as the 945a of usage data value Pa, Da, Sa and Ta is represented, and also can comprise alternatively out of Memory (for example, the identifier in indication Mobile data source).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, the different road segment segment of the different traffic that for example the part conduct of the road 925 between position 945a and 945b can be reported and predict.

With with mode like Fig. 9 category-A, Fig. 9 B has described example 905, its within 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 at the asterisk shown in position 945a, 945b and the 945c) that sent relevant traffic in 15 minutes in Mobile data source.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 front 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 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 these data values for each of data sample 910b in the continuous transmission of position 945b.Similarly, travel between position 945b and 945c such as the Mobile data source, then the Mobile data source obtains 15 data sample 910c1-910c15, and comprises fetched data value for each of 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 specific data sample data sample 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 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 becomes large between the data sample of this example of user in 1 minute given interval), and the average velocity of data sample 910b1-910b15 descends.Although 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 within 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 front 15 minutes the data from each of at least some data samples.Therefore, travel between position 965a and 965b such 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 common location (owing to not detecting movement for 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, in certain embodiments transmission can comprise all information 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 know that the Mobile data source is still not mobile between data sample 960b5 and 960b13).And, although do not illustrate here, but can omit in other embodiments the information of one or more such data samples, and can postpone follow-up transmission until 15 data samples that will be transmitted all are available (for example, if based on the data volume that will be sent out rather than the property transmission of performance period time).And, travel between position 965b and 965c such 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 is individual 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.Although do not illustrate here, but the Mobile data source (for example can also temporarily lose ability that basic device that usage data obtains 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, if need to would allow the take over party to insert or estimate these data samples), although can attempt in other embodiments (for example otherwise to obtain data sample, by determining the position with accurate not 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 the 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).

Although 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 be not with the particular course of determining for a plurality of data samples in specific Mobile data source to be 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 from 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 in certain embodiments such take over party to identify and relate to Mobile data sample source and/or indicate a plurality of data samples (for example, to determine to increase the privacy that relates to the Mobile data source based on design) from identical Mobile data source.

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 collection information of this road segment segment for 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 within this time period, if and each such data sample comprises speed and directional information (for example), then for this time period and be used for all data sources usually in the same direction mobile road segment segment can determine average gathering speed, for example to be similar to as the mode of the road traffic sensors of a plurality of vehicle collection information through sensors.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 is provided 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 from the south orientation track of expressway), with the road except one-way road 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 with two-way avenue as a road (for example, according to the average traffic for reporting and predict at the mobile vehicle of both direction), with each track of the expressway of multilane or other road as different logic road etc.

In certain embodiments, for the ease of determining interested road condition information with 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 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 of 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 relevant one or more indicated positions (for example, by, arrive, leave etc.) indexical relation etc.Similarly, the Mobile data source device can be configured in every way to carry out how and when transmitting or one or more data samples that obtain are provided, for example when the statistics predetermined condition, comprise the total distance based on covering from starting point, from last data sample obtain and/or transmit the distance of covering, play the T.T. of experience from the outset, from last data sample obtain and/or transmit time of experience, 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 the quantity of the buffer of storage data sample on the mobile device, or such as filling up or fill up in fact the quantity of measuring for the instruction time of transmission) 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 based on other data source 102 of the data source 101 of vehicle and/or Fig. 1 of other data source 388 (for example, subscriber equipment) 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 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 corresponding to the information of one or more data samples.Routine proceeds to step 810 and waits for, until obtain in time data sample, for example based on the parameter of retrieving and/or out of Memory (for example, passed through the indicated time quantum of past data sample acquisition, travelled the past data sample acquisition shown in distance, the indication obtain data sample etc. in continuous in fact mode).Routine then proceeds to step 815 obtaining data sample based on the movement in current location and Mobile data source, and stores data sample in step 820.If in step 825, determine also not arrive the time of the transmission of data, for example (for example measure the instruction time of the previous transmission of process based on the parameter of retrieving and/or out of Memory, the indication distance of having travelled and before having transmitted, indication if its available or with continuous in fact mode the transmission of data sample etc.), then routine is returned step 810.

Otherwise, routine proceed to step 830 with retrieval and select any stored because the data sample of previous transmission (or from beginning, from transmitting for the first time).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 obtaining only provides positional information, then be the average velocity that is used for 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 not corresponding to data sample of the actual movement in Mobile data source etc.), in other embodiments, also can not carry out such information removes.In step 845, routine is then to the current information of take over party's transmission in current group of data sample and the information of any collection that will use by rights.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.In the Mobile data source can not the embodiment and situation of the transmission of data, 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 until can the transmission of data or provide (for example, downloading via physics) because some or all of the data sample of previous transmission and obtained and storage in the Mobile data source.

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 in every way road traffic condition information, for example report current road traffic condition in substantially real-time mode, or with passing by and current next each predict future traffic at a plurality of future times of road traffic condition information.In certain embodiments, the future that can comprise various current, past and expection for generation of the type of the input data of future transportation condition predicting, and can comprise at predetermined time interval (for example from the output that prediction processing is come, three hours, or one day) in a plurality of future times each (for example, following per 5,15 or 60 minutes) prediction of the expection traffic that on each of interested a plurality of target track highway section, produces, describe in more detail such as institute elsewhere.For example, the type of input data can comprise following: about being used for the information of the current of each interested in geographic area target track highway section and the past 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 the following event that arranges (for example, the type of event, the start and end time of time expection, and/or the place of time or other position etc., for example be used for all events, the event of indication type, very great event is expected on the indicated threshold value attending etc. of (such as the attendants of 1000 or 5000 expections) such as having); 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, although in certain embodiments, a plurality of future times of predict future traffic are every points on time, but such prediction alternatively (for example can represent a plurality of time points in other embodiments, 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 credibility for the prediction that produces and/or 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 produce similarly with some of same type of input data in certain embodiments 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 about the 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 different future time section (for example, per hour rather than per 15 minutes).

Can also select in every way road and/or road segment segment for generation of future transportation condition predicting and/or forecast.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 in every way such geographic area, for example available the road traffic sensors network of at least some roads in this zone (for example, based on) and/or traffic congestion wherein are prominent questions easily based on current traffic condition information.In some such embodiment, road for generation of 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; Based on the road traffic regulation of carrying traffic, such as comprising Class I highway and the blocked road that can mainly be substituted into the road of larger capacities such as expressway and primary highway; Based on the functional category of road, such as 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 in every way the road segment segment for generation of future transportation condition predicting and/or forecast, 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, for example by composition group that the road traffic sensors of specific quantity is put together) of putting 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 that road travels, such as more detailed institute discussions elsewhere) from traffic sensor and/or other source; Deng.

In addition, in each embodiment, can use in a different manner future transportation condition predicting and/or forecast information, such as more detailed discussion ground elsewhere, be included in each time in every way (for example, by with communication to cellular mobile telephone and/or other portable consumer device; By showing information to the user, 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, such as 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, determine travel route and/or the time of suggestion with prediction and/or forecast information, 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 forecast information of a plurality of future times of one or more roads and/or road segment segment.

In addition, various embodiment (for example provide various mechanism and one or more traffic information systems for the user with other client, the data sample management system 350 of Fig. 3, RT information providing system 363, and/or information of forecasting provides system 360 etc.) mutual.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 the function that is provided by routine in certain embodiments can provide in mode alternatively as discussed abovely, for example can be divided in a plurality of routines or focuses on several routines.Similarly, the routine shown in certain embodiments can provide than described more function, for example when the routine shown in other alternatively lacks respectively or comprises such function, or works as the function quantity optional time that provides.In addition, although 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 concentrated in a data structure in a plurality of data structures or with a plurality of data structures.Similarly, the data structure shown in certain embodiments can be stored than described more or less information, for example when the data structure shown in other alternatively lacks respectively or comprises such information, or when quantity or the type optional time of institute's canned data.

Be understandable that from above-mentioned, although described specific embodiment for the purpose of example at this, can carry out various modifications in the situation that do not deviate from the spirit and scope of the present invention.Therefore, the present invention is except 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, although current only can being stated as in aspects more of the present invention is embedded in the computer-readable medium, similarly other side also can comprise.

Claims (46)

1. the method that computing machine is carried out is used for determining the traffic flow information of estimation for the vehicle that travels at these roads based on having reflected the data sample of travel conditions on the road, and described method comprises:

Receive the indication of one or more road segment segment 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 as follows the magnitude of traffic flow of the vehicle that travels on the inherent road segment segment of evaluation time section:

The related majority of the road segment segment that identification occurs within the described time period with report time is according to the group of sample;

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

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

Assist travelling on one or more roads with one or more in the estimation total quantity of vehicle.

2. according to claim 1 method, wherein, one or more for described at least one road segment segment, the probability estimation in the estimation total quantity of all vehicles that described road segment segment is travelled within the described time period comprises: the most probable total amount of determining all vehicles of travelling in described road segment segment within the described time period.

3. according to claim 1 method, wherein, one or more for described at least one road segment segment, the probability estimation of the estimation total quantity of all vehicles that travel in described road segment segment within the described time period comprises: the value of the confidence that is identified for estimating at least in part total quantity based on the possibility of estimation total quantity.

4. according to claim 1 method, wherein, one or more for described at least one road segment segment, probability estimation in the estimation total quantity of all vehicles that described road segment segment is travelled within the described time period comprises: determine the traffic arrival rate of getting on the bus more at least of the road segment segment within 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.

5. according to claim 4 method, 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 within 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.

6. according to claim 5 method, wherein, for described one or more road segment segment each, use described probability distribution to comprise as a part of determining the traffic arrival rate of the vehicle more at least of road segment segment in the described time period: to determine most probable traffic arrival rate.

7. according to claim 5 method, wherein, for described one or more road segment segment each, use described probability distribution to comprise as a part of determining the traffic arrival rate of the vehicle more at least of road segment segment in the described time period: based on the confidence level of described probabilistic distribution estimation in determined traffic arrival rate.

8. according to claim 5 method, wherein, for described one or more road segment segment each, described probability distribution is Poisson distribution.

9. according to claim 4 method, wherein, for each of described one or more road segment segment, more at least the traffic arrival rate of determining vehicle of the road segment segment within the described time period comprises the permeability factor of using described road segment segment within the described time period, described permeability factor has represented within the described time period in all vehicles that described road segment segment is travelled, and reports the estimation percentage of vehicle of described group data sample for described road segment segment.

10. according to claim 4 method, 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.

11. method according to claim 4, wherein, for each of described one or more road segment segment, be used for determining vehicle the traffic arrival rate road segment segment be all road segment segment more at least.

12. method according to claim 1, wherein, one or more for described at least one road segment segment, the probability 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 in described road segment segment within the described time period.

13. method according to claim 12, 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 in described road segment segment within the described time period.

14. method according to claim 12, wherein, for each of described one or more road segment segment, to the traffic arrival rate based on described road segment segment at least one determined described time period determined at least in part of the traffic density of described road segment segment.

15. method according to claim 14, also comprise each for described one or more road segment segment, determine at least one traffic arrival rate of described road segment segment within the described time period, in order to be illustrated in the vehicle more at least that arrives described road segment segment in the described time period.

16. method according to claim 14, wherein, for each of described one or more road segment segment, to the average traffic speed of estimation based on all vehicles that travel on the inherent described road segment segment of described time period determined also at least in part of the traffic density of described road segment segment.

17. method according to claim 16 also comprises each for described one or more road segment segment, the average traffic speed of all vehicles that estimation was travelled in described road segment segment within the described time period.

18. method according to claim 12, 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.

19. method according to claim 12 wherein, for each of described one or more road segment segment, determines to comprise the confidence level of estimating determined traffic density to the traffic density of described road segment segment.

20. method according to claim 1, wherein, one or more for described at least one road segment segment, probability estimation in the estimation total quantity of all vehicles that described road segment segment is travelled within the described time period comprises: determine the occupation due to communication rate more at least on the inherent described road segment segment of described time period, determined occupation due to communication rate is at least in part based on the estimation total quantity of all vehicles that travel in described road segment segment within the described time period.

21. method according to claim 20, wherein, for each of described one or more road segment segment, more at least the occupation due to communication rate of determined described road segment segment represented within the described time period, number percent averaging time that described at least one vehicle that is travelled in described road segment segment more at least takies.

22. method according to claim 20, wherein, for each of described one or more road segment segment, to determining at least in part 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 of the occupation due to communication rate of described road segment segment.

23. method according to claim 22, wherein, for each of described one or more road segment segment, at least one traffic arrival rate based on described road segment segment in the determined described time period determined also at least in part of the occupation due to communication rate of described road segment segment.

24. method according to claim 23, wherein, for each of described one or more road segment segment, to the average traffic speed of estimation based on all vehicles that travel on the inherent described road segment segment of described time period determined also at least in part of the occupation due to communication rate of described road segment segment.

25. method according to claim 24, also comprise each for described one or more road segment segment, determine at least one traffic density of described road segment segment within the described time period, determine at least one traffic arrival rate of described road segment segment within the described time period, and estimate the average traffic speed of all vehicles that within the described time period, travel in described road segment segment.

26. method according to claim 20, wherein, for each of described one or more road segment segment, determining to comprise and determine most probable occupation due to communication rate the occupation due to communication rate of described road segment segment.

27. method according to claim 20 wherein, for each of described one or more road segment segment, determines to comprise the confidence level of estimating determined occupation due to communication rate to the occupation due to communication rate of described road segment segment.

28. method according to claim 1 wherein, for each of described one or more road segment segment, is carried out estimation to the magnitude of traffic flow of the vehicle that travels in described road segment segment in each of a plurality of different time periods within the described time period.

29. method according to claim 1, wherein, for each of described one or more 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 within the described time period, travels in described road segment segment, in order to use at least some of associated data sample of described road segment segment for each of described a plurality of time windows.

30. method according to claim 29, 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.

31. method according to claim 1, wherein, for each of described one or more road segment segment, estimation to the total quantity of all vehicles of travelling in described road segment segment within the described time period comprises: be identified for estimating at least one the value of the confidence of total quantity, and wherein the usefulness of one or more estimation total quantitys of vehicle comprised with the one or more of determined the value of the confidence and assist travel the future on one or more roads.

32. method according to claim 1, wherein, the use of one or more estimation total quantitys of vehicle comprised to one or more people provide indication to one or more estimation total quantitys of vehicle, with auxiliary people considering travelling on described one or more roads.

33. method according to claim 32, wherein, with with respect to the data sample that receive to be used for estimation real-time mode basically, carry out to the estimation of one or more estimation total quantitys with to the use of one or more estimation total quantitys, in order to allow the people can basically carry out real-time considering.

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

The first assembly is configured to each for a plurality of roads, receives the indication of a plurality of data samples related with road, and each data sample comprises the information that has represented the traveling state of vehicle on road; With

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

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

Based on determined vehicle fleet size, be created in the probability 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 indication to the estimation magnitude of traffic flow to be used for travelling on the service road.

35. computing system according to claim 34, wherein, for in a plurality of roads at least some each, in a plurality of data samples that are associated with road each is 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 subset of all vehicles of travelling on the inherent described road of time period of described road by the vehicle of the information representative of the data sample related with described road, generation to probabilistic estimation of the magnitude of traffic flow of all vehicles of travelling at described road comprises: 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 on the estimation magnitude of traffic flow to be used for assisting travelling on described road to comprise: as the driver of vehicle represents that the information of the relevant estimation magnitude of traffic flow is to be used for affecting the decision of relevant road driving.

36. computing system according to claim 35, wherein, for each of at least some roads, the probability estimation that is created in the magnitude of traffic flow of all vehicles that travel on the described road also comprises: by within the time period of described road, determining the average percent occupancy of certain point on described road at the vehicle that described road travels, and the determining at least in part based on determined estimation arrival rate of average percent occupancy.

37. computing system according to claim 34, wherein, each of described the 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.

38. computing system according to claim 34, wherein, described the 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 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 that its travel conditions is by the quantity of the vehicle of the information representative of the data sample related with road, be created at least in part the probability estimation of the magnitude of traffic flow of all vehicles that travel on the inherent road of time period 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.

39. the method that computing machine is carried out is used for based on determining the estimation traffic flow information by the data sample of the vehicle report of travelling on the road, described data sample comprises the data of relevant described vehicle traveling information, and described method comprises:

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

For within 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:

Within the described time period, receive the information of the current traffic condition of relevant a plurality of road segment segment, the information that receives 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 within 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 subset of all vehicles of travelling in described road segment segment within the described time period, the information that receives 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, automatically estimate as follows the traffic flow information of all vehicles on described road segment segment that travel in the described time period:

Identify the majority of described road segment segment in the described time period according to the group of sample, described majority according to sample from a plurality of data samples and a plurality of additional data sample at least one;

Determine that the vehicle number corresponding with described group data sample, described corresponding vehicle are the subsets 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 in the most probable traffic arrival rate at described road segment segment place, determine that the traffic arrival rate is at least in part based on the probability distribution that produces for the determined vehicle fleet size of data sample of described group of report;

Determine the most probable traffic density of described road segment segment, in order to 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 more at least most probable number percent occupation due to communication rate, determine that number percent occupation due to communication rate is at least in part based on determined traffic density; With

Assist travel the future on described one or more roads with the traffic arrival rate in the determined described time period, traffic density and number percent occupation due to communication rate,

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.

40. method according to claim 39, wherein, for each of at least one described time period and each of at least one described road segment segment, the identification group of a plurality of data samples of described road segment segment comprises in the described time period: according to sample, the reported position of described data sample is positioned on the described road segment segment from the majority of a plurality of data samples in the described time period; 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;

Wherein, the traffic flow information of estimating 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 at least in part all vehicles that travel on the inherent described road segment segment of described time period based on the report speed of described group data sample.

41. method according to claim 40, wherein, for each of at least one described time period and each of at least one described road segment segment, the traffic density of determining described road segment segment for all vehicles that travel in described road segment segment within the described time period is also at least in part based on the average traffic speed of estimation of all vehicles that inherent described road segment segment of described time period is travelled.

42. method according to claim 41, wherein, for each of at least one described time period and each of at least one described road segment segment, determine all vehicles of within the described time period, travelling in described road segment segment for the number percent occupation due to communication rate more at least of described road segment segment also at least in part based on the estimation average length of all vehicles that the average traffic speed of estimation of all vehicles that inherent described road segment segment of described time period is travelled and inherent described road segment segment of described time period are travelled.

43. method according to claim 42, wherein, for each of at least one described time period and each of at least one described road segment segment, determine that the traffic arrival rate at described road segment segment place of all vehicles of travelling on the inherent described road segment segment of described time period is also at least in part based on the estimation number percent of the vehicle corresponding with described group data sample in all vehicles that travel on the inherent described road segment segment of described time period.

44. method according to claim 39, wherein, for each of at least one described time period and each of described at least one road segment segment, 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 for 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 within the described time period.

45. method according to claim 39, wherein, estimate the traffic flow information of all vehicles on described road segment segment that travel in the described time period with the data sample that recently receives in real-time mode, also in basically real-time mode, use at least some determined traffic arrival rate, determined traffic density and determined number percent occupation due to communication rate in the described time period, to assist travelling that described one or more roads are about to.

46. method according to claim 45, wherein, use at least some determined traffic arrival rate, determined traffic density and determined number percent occupation due to communication rate in the described time period to comprise: will offer about the information of determined traffic arrival rate, traffic density and number percent occupation due to communication rate one or more people that consideration will be travelled at described one or more roads.

Images