CN103886756B - Based on the freeway network method for detecting operation state of OBU - Google Patents
Based on the freeway network method for detecting operation state of OBU Download PDFInfo
- Publication number
- CN103886756B CN103886756B CN201410153549.4A CN201410153549A CN103886756B CN 103886756 B CN103886756 B CN 103886756B CN 201410153549 A CN201410153549 A CN 201410153549A CN 103886756 B CN103886756 B CN 103886756B
- Authority
- CN
- China
- Prior art keywords
- obu
- data
- travel time
- nodes
- road
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000002159 abnormal effect Effects 0.000 claims description 19
- 238000004364 calculation method Methods 0.000 claims description 19
- 238000007781 pre-processing Methods 0.000 claims description 7
- 238000011144 upstream manufacturing Methods 0.000 claims description 6
- 230000001360 synchronised effect Effects 0.000 claims description 5
- 238000001514 detection method Methods 0.000 abstract description 16
- 238000012544 monitoring process Methods 0.000 abstract 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 6
- 101001093748 Homo sapiens Phosphatidylinositol N-acetylglucosaminyltransferase subunit P Proteins 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
<b> mono-kind is based on the freeway network method for detecting operation state of </b><bGreatT.Gre aT.GTOBU</b><bGr eatT.GreaT.GT, it adopts charge station </b><bGreatT.Gre aT.GTETC</b><bGr eatT.GreaT.GT track </b><bGreatT.Gre aT.GTOBU</b><bGr eatT.GreaT.GT detecting device and trackside </b><bGreatT.Gre aT.GTOBU</b><bGr eatT.GreaT.GT detector acquisition road section traffic volume Flow Velocity, road network runs the predicted travel time between index and section, and informed vehicle.The data of charge station </b><bGreatT.Gre aT.GTETC</b><bGr eatT.GreaT.GT track </b><bGreatT.Gre aT.GTOBU</b><bGr eatT.GreaT.GT detecting device combine with the data of trackside </b><bGreatT.Gre aT.GTOBU</b><bGr eatT.GreaT.GT detecting device and improve the accuracy of freeway network real-time traffic condition monitoring result by detection method of the present invention, and the reliability of travel time estimation between charge station, thus improve ability to supervise and the service level of vehicle supervision department.</b>
Description
Technical Field
The invention relates to a method for detecting the running state of a highway network, in particular to a method for detecting the running state of the highway network based on an OBU (on-board unit).
Background
China organizes the key technical research and application of national highway non-stop toll collection service from 2005, and by 2013, a unified non-stop toll collection system (also called Electronic Toll Collection (ETC) covers 26 provinces and cities, and users reach over 600 thousands.
In terms of OBU application, patent document 1 (CN 202523116U) discloses a non-stop highway driving path recording system based on a mobile network, which detects an OBU module through an RSU module, reads information of a temporary path information table, i.e. a mobile base station number, and determines a real path that a vehicle has traveled through by recording the mobile base station number covering a highway, thereby effectively solving the problems of ambiguity or ambiguity in the current situation of highway charging, time consumption for parking and traffic jam when confirming the driving path. However, this recording system determines the real route traveled by a single vehicle through the mobile base station for the purpose of solving the "ambiguity" in the charging status, but due to its purpose, it cannot analyze the operation state of the road network as a whole efficiently, and it cannot realize effective detection of the road network state and avoiding vehicle congestion
Patent document 2 (CN 102426349A) discloses a vehicle positioning method and device for an ETC system, and a non-stop toll collection system, and states position data of a detected vehicle corresponding to an OBU through an angle of a captured microwave signal beam emitted by the OBU, so that longitudinal positioning of the ETC vehicle is realized, a physical position of a vehicle-mounted electronic tag in a communication area is accurately determined, and information of a vehicle without the electronic tag or a tag vehicle with unsuccessful transaction in the electronic toll collection system is found in cooperation with a video capture system, so that inspection of violation of the ETC vehicle is realized, and toll is chased. Because the positioning device is used for checking and positioning unpaid vehicles, the positioning device cannot efficiently analyze the operation state of a road network on the whole, and cannot effectively detect the state of the road network and avoid vehicle congestion.
Patent document 3 (CN 103258428A) discloses a method for realizing traffic state collection based on ETC devices, which reasonably arranges a plurality of traffic information collection devices based on DSRC along a highway, utilizes ETC vehicle-mounted devices loaded by vehicles, and the signal coverage area of the traffic information collection devices based on DSRC is awakened by microwave signals continuously transmitted, and generates information interaction process with the traffic information collection devices, and a background management center analyzes the traffic flow and congestion degree of the current road section by uploading data, thereby solving the technical problem that the ETC vehicle-mounted terminal is used as an information collection source to realize services such as traffic flow statistics, traffic congestion degree analysis and the like. However, this method only uses roadside detection to realize statistical analysis of road network state, and only can reflect the traffic operation state of a road between two DSRC information acquisition points in one trip, and cannot reflect the influence of factors such as entrance deceleration, ramp travel time, clock synchronization, and the like, so that it cannot comprehensively and reliably reflect the real operation condition of the expressway network, and also cannot provide a reliable travel time estimation between two toll stations for expressway travelers, thereby failing to effectively improve the expressway network operation supervision capability and service level of a traffic management department.
Therefore, the traditional traffic detectors (coils, microwaves and ultrasonic waves) can only detect section traffic flow parameters (such as traffic volume, section speed and the like), can not directly obtain the road section traffic flow speed, the road section average driving time, road network congestion, the road network running state and the like, can be obtained only by densely distributing the detectors and matching with complex calculation, and have poor accuracy. Although the roadside OBU detector has the advantages of low cost, convenience in installation (installation on a gantry or roadside), small required power supply, adaptability to various data transmission modes and the like, the road traffic flow speed, the average running time, the road network congestion and the road network running state cannot be directly obtained, and the influence of unreliable data caused by overspeed, parking and the like of individual vehicles cannot be avoided.
Disclosure of Invention
The invention aims to provide a method for detecting the running state of an expressway network based on novel expressway data acquisition equipment, which provides timely and accurate road network running information of the expressway network for traffic managers and effectively improves the service level of public travelers.
In order to achieve the purpose, the invention provides an OBU-based highway network operation state detection method.
According to a first aspect of the present invention, the method for detecting the operation status of an OBU-based highway network comprises the following steps:
firstly, an OBU detector is arranged on a highway, and clocks of all the OBU detectors are synchronized, wherein the OBU detector comprises an ETC lane OBU detector arranged at a toll station and a roadside OBU detector arranged between adjacent toll stations, a road section between the toll stations is formed between the adjacent ETC lane OBU detectors, an inter-node road section is formed between the adjacent ETC lane OBU detectors, the OBU detector acquires an OBU unique identification code and time thereof, and data comprising the OBU unique identification code, an entrance, entrance time, an exit and exit time are acquired;
secondly, acquiring data passing through an adjacent OBU detector through the adjacent OBU detector, and matching the OBU detector with data of an adjacent upstream OBU detector according to the unique OBU identification code to obtain data of the same unique OBU identification code;
thirdly, calculating speed data of road sections between toll stations and road sections between nodes of the same OBU unique identification code according to the data of the same OBU unique identification code, eliminating abnormal data of the speed data of the road sections between toll stations and the road sections between nodes of all the OBU unique identification codes in a time period by adopting a bit value division method through a data preprocessing unit, respectively taking an arithmetic average value of the speed data processed by the abnormal data as an average speed of the road sections between toll stations and an average speed of the road sections between nodes, and determining respective traffic running states according to the average speeds(ii) a Meanwhile, according to the data of the same OBU unique identification code, calculating the travel time data of the road section between the nodes of the same OBU unique identification code, eliminating abnormal data of the travel time data of the road section between the nodes of all the OBU unique identification codes in a time period by a data preprocessing unit through a bit value division method, taking the arithmetic mean value of the travel time data after being processed by the abnormal data as the average travel time of the road section between the nodes, and calculating the estimated travel time of the road section between the toll stations according to the average travel time;
and fourthly, informing the traffic running state of the road section between the current toll stations and the predicted travel time of the road section between the toll stations when the vehicle enters the toll stations, and informing the traffic running state of the road section between the current nodes and the predicted travel time when the vehicle enters the road section between the nodes.
The method of the present invention according to the first aspect, wherein the determination of the predicted travel time of the section between the toll stations is performed by:
step 1, input
；
Wherein
The estimated travel time value of the section between the first i nodes from the toll station 1 in the t-th time period is represented; i is the serial number of the road section between the nodes, and the minimum calculation is started from 1;
step 2, order
If, if
Then, then
And if not, the step (B),
；
wherein t represents the t-th time period,
to represent
The integer part of the ratio of (a) to (b),
represents the average travel time of the i +1 th inter-node link during the t-th time period,
representing the average travel time of the i +1 th internode road section in the t + d th time period;
step 2, judging whether the sum of the travel time of the road sections among the previous i nodes is more than or equal to the time period T, if d is more than or equal to 1, the time is already in the T + d period when the vehicle reaches the road section among the i +1 nodes, and selecting the average travel time in the T + d period
As the travel time of the i +1 th inter-node link; otherwise, the average travel time T in the T-th time period is selected
i+1
(t) as the travel time for the i +1 th inter-node link;
in the step 3, the step of,
；
;
step 3, calculating travel time estimated values of the sections between the first i +1 nodes;
step 4, if
Then ending, wherein N is the number of the internode sections between the toll station 1 and the toll station m, and outputting
I.e. the estimation result of the travel time from the toll station 1 to the toll station m in the t-th time period, otherwise, turning to the step 1.
The method according to the present invention according to the first or second aspect, wherein the determining of the respective traffic operation state and the traffic operation index of the road network in the third step comprises the steps of: and determining respective traffic operation states according to the average speed of the road sections between the toll stations and the average speed of the road sections between the nodes, wherein the traffic operation states comprise smooth, basically smooth, light congestion, moderate congestion and severe congestion, and determining corresponding road network traffic operation indexes according to the traffic operation states.
The method according to the present invention in the third aspect, wherein the heavily congested mileage proportion is determined according to a traffic running state.
The invention has the following beneficial effects: an OBU detector is added at the side of the highway, clocks of the OBU detectors at the side of the highway are synchronous, the running track information of vehicles can be effectively obtained, the traffic flow condition on the highway section can be mastered in time, and the problem that the real-time traffic running state detection equipment on the current highway network is insufficient is solved; the data of the ETC lane OBU detector of the toll station and the data of the road side OBU detector are combined, so that the accuracy of the real-time traffic running state detection result of the highway network is improved, the real running condition of the highway network is reliably reflected, the reliability of travel time estimation between toll stations is improved, and the reliability of traffic management departments is improvedThe expressway network operation supervision capability and service level.
Drawings
Fig. 1 shows a general view of an OBU-based highway network operational status detection method according to the present invention;
FIG. 2 shows a road segment division diagram for real-time traffic status detection;
FIG. 3 illustrates various scenarios of roadside OBU detector placement locations;
fig. 4 shows a road segment division diagram for travel time estimation.
Detailed Description
The invention will be further illustrated with reference to the following examples:
as shown in FIG. 1, the running state detection method of the invention comprises real-time traffic running state detection and travel time estimation between the start point and the stop point based on historical data, and the two parts realize data information credibility through data preprocessing.
The detectors include two types of roadside OBU detectors and the ETC lane OBU detector of the toll station, and the roadside OBU detector and the ETC lane OBU detector are synchronized in clock.
Now, the real-time traffic status detection will be explained first.
The real-time traffic state detection is realized by determining the division rule of road sections, matching the data of the OBU detectors, calculating the average travel time and the average speed between adjacent detectors, and calculating the average speed of the road sections between adjacent toll stationsThe calculation and the division of the operation state, the calculation of the traffic operation index of the highway network, and the like are explained. The method comprises the following specific steps:
1) division principle of road sections
One of the purposes of real-time traffic operation state detection is to better provide travel information service for the public, so that the highway between two adjacent toll stations is defined as a section between the toll stations in this step. The division of the road section is schematically shown in fig. 2.
) Data matching method
This data match is for an adjacent detector, the location where the vehicle reaches the detector is called the exit, and the location where it passes an adjacent upstream detector is called the entrance. And extracting data of the previous time period T at the current moment by taking the outlet detector as a reference, and matching the data with data of an inlet detector (including an OBU detector at a roadside and an ETC lane at a toll station) adjacent to the upstream according to the unique identification code data of each OBU to obtain data of an inlet, an outlet, inlet time and outlet time aiming at the same OBU unique identification code.
) Average velocity between adjacent detectors
The first step is as follows: calculating the travel time of each piece of data;
according to the data matching method, data arriving at the exit detector within a time period T is matched. And if the length of the road section of the adjacent detector is set to be L and n matched data exist in one time period T, the travel time calculation formula of the ith data is as follows:
the second step is that: calculating the driving speed of each piece of data;
the third step: abnormal data culling
And calculating to obtain the running speed data of the n pieces of data. The analysis shows that the running speeds of some vehicles which stop at the road side or have a rest in a service area have certain difference in the same road section, the running speeds of some vehicles which stop at the road side are obviously smaller, the running speeds of some vehicles which run at overspeed are obviously smaller, the observation data which obviously deviate from the measurement totality are defined as abnormal data, and the abnormal data are further eliminated by utilizing a quantile value method. Selecting a 5% quantile value and a 95% quantile value for the expressway to remove abnormal data, wherein the mathematical expression is
The fourth step: calculation of average speed of road segments between adjacent detectors (i.e., inter-nodal road segments)
M (m) remains in the time period T after the abnormal data are eliminated
) Bar data, then the average time-of-flight for the road segment between adjacent OBU detectors is,
and the ith speed data in the data set after the abnormal data is removed.
) Average speed calculation of road sections between adjacent toll stations (i.e., road sections between toll stations)
According to the existing infrastructure condition of the expressway and the limitation of the layout cost of the detectors, the layout modes of the roadside OBU detectors mainly include three types: 2 roadside OBU detectors are arranged between two adjacent toll stations; 1 roadside OBU detector is arranged between two adjacent toll stations; and no roadside OBU detector is arranged between two adjacent toll stations. The schematic layout of the roadside OBU detector is shown in FIG. 3. The calculation method of the link average speed is described below based on the above three cases.
Case 1: there are 2 OBU detectors between two adjacent toll booths
In the present invention, the mileage between the detectors D1 and D2 is used
Units representing, mileage: kilometers; average velocity between detectors D1 and D2
Represents, unit: kilometers per hour. The following formulae have the same symbolic meanings as above. And selecting different calculation formulas according to the position relation between the roadside OBU detector and the toll stations to calculate the average speed of the road sections between the adjacent toll stations.
And is
When the temperature of the water is higher than the set temperature,
and is
When the temperature of the water is higher than the set temperature,
in the formula,
indicating toll station
And toll station
The spatial average speed of the corresponding main section,
wherein,
indicating that the detector is in a time period
Cross-sectional flow rate (number of ETC vehicles passing through),
is represented by a detector
To the detector
The flow rate of (a) to (b),
indicating by toll station
To the detector
The flow rate of (a) to (b),
indicating by toll station
To the detector
The spatial average velocity of (a).
And is
When the temperature of the water is higher than the set temperature,
when in use
And is
When the temperature of the water is higher than the set temperature,
wherein,
the calculation formula (2) is shown as formula (1).
Case 2: there are 1 OBU detector between two adjacent toll booths
When in use
When the temperature of the water is higher than the set temperature,
wherein,
the calculation formula (2) is shown as formula (1).
When in use
When the temperature of the water is higher than the set temperature,
case 3: there is no OBU detector between two adjacent toll stations
Wherein,
indicating a toll station within a time period T
Cross-sectional flow rate (number of ETC vehicles passing through),
is represented by a detector
To toll station
The flow rate of (a) to (b),
indicating by toll station
To toll station
The flow rate of (a) to (b),
indicating by toll station
To toll station
The spatial average velocity of (a).
) Division of road traffic operating states
4) obtaining the average speed V of the road sections between adjacent toll stations, and obtaining the free-stream driving speed V according to the corresponding design hourly speed or statistics
0
Obtaining a travel time index f of the road section, wherein the calculation formula is as follows:
aiming at the travel time index, the traffic running states of the road sections are divided, and the grade division standard is as follows:
6) traffic operation index of expressway network
The Traffic Operation Index (TOI) of the expressway network ranges from 0 to 10 and is divided into five grades. The traffic jam detection method comprises the following steps of 0-2, 2-4, 4-6, 6-8 and 8-10, wherein the five levels of 'unblocked', 'basically unblocked', 'light jammed', 'medium jammed' and 'severe jammed' correspond to the above steps respectively, and the higher the numerical value is, the more serious the traffic jam condition is.
According to the division standard of the congestion degree, the proportion of mileage of the severely congested road sections in the expressway network can be obtained. The calculation formula is as follows:
the recommended conversion relationship between the proportion of the heavily congested mileage of the highway network and the TOI is shown in table 1.
TABLE 1 recommendation conversion relation table for severe congestion mileage proportion and TOI of highway network
As shown in table 1, the severe congestion mileage proportion and the road network traffic operation index are in a linear relationship, and the specific calculation method of the traffic operation index is as follows:
setting severe congestionThe range of the proportion of the blocked mileage is [ x0, x1 ]]The corresponding traffic operation index interval is [ y0, y1 ]]The mathematical relational expression of the traffic operation index y and the severe congestion mileage ratio x is
The function relation of the traffic operation index y and the serious congestion mileage proportion x obtained by the method is
Travel time estimation based on historical data will be described below.
Travel time estimation based on historical data will be described through four aspects of road segment division principle determination, data matching method, road segment average travel time calculation and travel time estimation. As shown in fig. 1. The method comprises the following specific steps:
1) determination of a road segment division principle
The method is characterized in that an OBU detector and a roadside OBU detector of an ETC lane of a toll station are used as nodes, and road sections of an expressway are divided, wherein a specific road section division schematic diagram is shown in figure 4.
) Data matching
This data match is also a match for an adjacent detector, the location where the vehicle reaches the detector is called the exit, and the location where it passes an adjacent upstream detector is called the entrance. Different from real-time traffic state detection, the method takes an entrance detector as a reference, extracts data of a time period T, matches the data with data of an exit detector (including an OBU detector at a roadside or an ETC lane at a toll station) adjacent to the downstream according to the unique identification code data of each OBU, and obtains one unique identification code of the same OBU, wherein the unique identification code of the same OBU comprises an entrance and an exitData of entry time, exit time.
) Segment mean time of flight calculation between adjacent OBU detectors
Similar to the calculation process of 3) the average speed between the adjacent detectors in (1), the travel time data obtained by matching in the time period T is subjected to abnormal data elimination by a quantile value method, and then the arithmetic average of the remaining data is calculated as the average travel time of the section between the adjacent detectors in the time period T.
) Road segment average travel time estimation between selected start and stop toll stations
Taking fig. 4 as an example, assuming that the section between the toll station 1 and the toll station m is divided into N sub-sections, 3) the travel time results of each section in different time periods can be obtained, as shown in table 2.
TABLE 2 calculation of travel time for each sub-segment
Since the running state of the road network changes along with the change of time, the average travel time of each sub-road section in different time periods is different, and therefore, in the patent, a travel time estimation method based on space-time correlation is adopted for estimation and analysis. Taking fig. 4 as an example, the travel time estimation process between the toll booth 1 and the toll booth m is explained. On the basis of the calculated average travel time of the sub-sections per time period, the following steps are carried out to obtain an estimated travel time value of the travel time from the toll station 1 to the toll station m in the t-th time period.
The method comprises the following specific steps:
first step input
；
Wherein
An estimated travel time value representing the travel time of the first i road sections from the toll station 1 (starting toll station) in the t-th time period; i is the link number, and the minimum is calculated from 1.
The second step is to
If, if
Then, then
And if not, the step (B),
；
wherein t represents the t-th time period (the minimum value is 1),
to represent
The integer part of the ratio of (a) to (b),
represents the average travel time of the i +1 th road segment in the t-th time period,
represents the average travel time of the (i + 1) th link during the (t + d) th time period.
The step is to judge whether the sum of the travel time of the first i road sections isIf d is more than or equal to 1, the time is in the T + d th period when the vehicle reaches the i +1 th road section, and the average travel time in the T + d th period is selected
As the travel time of the (i + 1) th link; otherwise, the average travel time in the t-th time period is selected
As the travel time of the (i + 1) th link.
Step 3
；
;
The step is to calculate the travel time estimated value of the first i +1 road sections.
Step 4 if
Then, end and output
I.e. the estimation result of the travel time from the toll station 1 to the toll station m in the t-th time period, otherwise, turning to the step 1.
Therefore, the invention provides a method for detecting the running state of the highway network based on the OBU, which comprises the following steps:
the method comprises the steps of arranging an OBU detector on a highway, and enabling clocks of all the OBU detectors to be synchronous, wherein the OBU detector comprises an ETC lane OBU detector arranged at a toll station and a road arranged between adjacent toll stationsThe system comprises side OBU detectors, wherein a road section between toll stations is formed between the adjacent ETC lane OBU detectors, an inter-node road section is formed between the adjacent ETC lane OBU detectors, and the OBU detectors acquire an OBU unique identification code and time thereof and acquire data comprising the OBU unique identification code, an entrance, entrance time, an exit and exit time;
secondly, acquiring data passing through an adjacent OBU detector through the adjacent OBU detector, and matching the OBU detector with data of an adjacent upstream OBU detector according to the unique OBU identification code to obtain data of the same unique OBU identification code;
thirdly, calculating speed data of road sections between toll stations and road sections between nodes of the same OBU unique identification code according to the data of the same OBU unique identification code, eliminating abnormal data of the speed data of the road sections between toll stations and the road sections between nodes of all the OBU unique identification codes in a time period by a data preprocessing unit through a bit value division method, respectively taking arithmetic mean values of the speed data and the travel time data after abnormal data processing as average speeds of the road sections between toll stations and average speeds of the road sections between nodes, and determining respective traffic running states according to the arithmetic mean values; meanwhile, according to the data of the same OBU unique identification code, calculating the travel time data of the road section between the nodes of the same OBU unique identification code, eliminating abnormal data of the travel time data of the road section between the nodes of all the OBU unique identification codes in a time period by a data preprocessing unit through a bit value division method, taking the arithmetic mean value of the travel time data after being processed by the abnormal data as the average travel time of the road section between the nodes, and calculating the estimated travel time of the road section between the toll stations according to the average travel time;
and fourthly, informing the traffic running state of the road section between the current toll stations and the predicted travel time of the road section between the toll stations when the vehicle enters the toll stations, and informing the traffic running state of the road section between the current nodes and the predicted travel time when the vehicle enters the road section between the nodes.
For the technology of the technical fieldThe inventive concept can be implemented in different ways, as the technology evolves. The embodiments of the invention are not limited to the above-described embodiments but may vary within the scope of the claims.
Claims (3)
1. An OBU-based method for detecting the running state of a highway network comprises the following steps:
firstly, an OBU detector is arranged on a highway, and clocks of all the OBU detectors are synchronized, wherein the OBU detector comprises an ETC lane OBU detector arranged at a toll station and a roadside OBU detector arranged between adjacent toll stations, a road section between the toll stations is formed between the adjacent ETC lane OBU detectors, an inter-node road section is formed between the adjacent ETC lane OBU detectors, the OBU detector acquires an OBU unique identification code and time thereof, and data comprising the OBU unique identification code, an entrance, entrance time, an exit and exit time are acquired;
secondly, acquiring data passing through an adjacent OBU detector through the adjacent OBU detector, and matching the OBU detector with data of an adjacent upstream OBU detector according to the unique OBU identification code to obtain data of the same unique OBU identification code;
thirdly, calculating speed data of road sections between toll stations and road sections between nodes of the same OBU unique identification code according to the data of the same OBU unique identification code, eliminating abnormal data of the speed data of the road sections between toll stations and the road sections between nodes of all the OBU unique identification codes in a time period by adopting a bit value division method through a data preprocessing unit, respectively taking an arithmetic average value of the speed data processed by the abnormal data as an average speed of the road sections between toll stations and an average speed of the road sections between nodes, and determining respective traffic running states according to the arithmetic average value; meanwhile, according to the data of the same OBU unique identification code, calculating the travel time data of the road section between the nodes of the same OBU unique identification code, eliminating abnormal data of the travel time data of the road section between the nodes of all the OBU unique identification codes in a time period by a data preprocessing unit through a bit value division method, taking the arithmetic mean value of the travel time data after being processed by the abnormal data as the average travel time of the road section between the nodes, and calculating the estimated travel time of the road section between the toll stations according to the average travel time;
step four, when the vehicle enters the toll station, informing the traffic running state of the road section between the current toll station and the predicted travel time of the road section between the toll station, and when the vehicle enters the road section between the nodes, informing the traffic running state of the road section between the current nodes and the predicted travel time of the road section between the nodes;
the method comprises the following steps of:
step 1, input；
WhereinThe estimated travel time value of the section between the first i nodes from the toll station 1 in the t-th time period is represented; i is the serial number of the road section between the nodes, and the minimum calculation is started from 1;
step 2, orderAnd if not, the step (B),
wherein t represents the t-th time period,the integer part of the ratio of (a) to (b),represents the average travel time of the i +1 th inter-node link during the t-th time period,representing the average travel time of the i +1 th internode road section in the t + d th time period;
step 2, judging whether the sum of the travel time of the road sections among the previous i nodes is more than or equal to the time period T, if d is more than or equal to 1, the time is already in the T + d period when the vehicle reaches the road section among the i +1 nodes, and selecting the average travel time in the T + d periodAs the travel time of the i +1 th inter-node link; otherwise, the average travel time T in the T-th time period is selectedi+1(t) as the travel time for the i +1 th inter-node link;
in the step 3, the step of,
step 3, calculating travel time estimated values of the sections between the first i +1 nodes;
step 4, ifThen ending, wherein N is the number of the internode sections between the toll station 1 and the toll station m, and outputtingI.e. the estimation result of the travel time from the toll station 1 to the toll station m in the t-th time period, otherwise, turning to the step 1.
2. The method of claim 1, wherein determining the respective traffic operation state in the third step comprises the steps of: and determining respective traffic operation states according to the average speed of the road sections between the toll stations and the average speed of the road sections between the nodes, wherein the traffic operation states comprise smooth, basically smooth, light congestion, moderate congestion and severe congestion, and determining corresponding road network traffic operation indexes according to the road section traffic operation states.
3. The method of claim 2, wherein the heavily congested mileage proportion is determined according to a road section traffic operation state.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410153549.4A CN103886756B (en) | 2014-04-17 | 2014-04-17 | Based on the freeway network method for detecting operation state of OBU |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410153549.4A CN103886756B (en) | 2014-04-17 | 2014-04-17 | Based on the freeway network method for detecting operation state of OBU |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103886756A CN103886756A (en) | 2014-06-25 |
| CN103886756B true CN103886756B (en) | 2015-12-30 |
Family
ID=50955624
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410153549.4A Expired - Fee Related CN103886756B (en) | 2014-04-17 | 2014-04-17 | Based on the freeway network method for detecting operation state of OBU |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103886756B (en) |
Families Citing this family (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6179545B2 (en) * | 2015-03-25 | 2017-08-16 | トヨタ自動車株式会社 | Traffic jam information generating apparatus and traffic jam information generating method |
| CN105489010B (en) * | 2015-12-29 | 2019-01-04 | 中国城市规划设计研究院 | A kind of through street journey time reliability monitoring analysis system and method |
| CN105590346B (en) * | 2016-02-18 | 2018-01-16 | 华南理工大学 | The traffic information collection of turn pike net and inducible system based on path identifying system |
| CN106023600B (en) * | 2016-06-21 | 2019-05-21 | 广州地理研究所 | A kind of vehicle speed detection method and device based on vehicle electron identifying |
| CN106571037A (en) * | 2016-11-15 | 2017-04-19 | 同济大学 | Station detection technology-based expressway real-time road condition monitoring method |
| CN106710216B (en) * | 2017-02-28 | 2019-08-30 | 广东省交通运输档案信息管理中心 | Highway real-time traffic congestion road conditions detection method and system |
| CN106816009B (en) * | 2017-02-28 | 2019-06-21 | 广东省交通运输档案信息管理中心 | Highway real-time traffic congestion road conditions detection method and its system |
| CN107527501A (en) * | 2017-06-05 | 2017-12-29 | 交通运输部公路科学研究所 | The building method of travel time data and the method for predicting the motorway journeys time between a kind of highway station |
| CN107293117B (en) * | 2017-07-04 | 2019-08-09 | 清华大学 | A kind of judgment method of highway anomalous event |
| CN108694749A (en) * | 2018-04-11 | 2018-10-23 | 东南大学 | A kind of identification of Expressway Multi-path and traffic state estimation method and its system |
| CN108960465A (en) * | 2018-06-29 | 2018-12-07 | 东南大学 | It is a kind of to consider that the parking lot for being expected service level selects reservation system and method |
| CN109996174B (en) * | 2019-04-16 | 2020-12-18 | 江苏大学 | Road section real-time scoring method for vehicle-mounted self-organizing network content routing |
| CN111815100B (en) * | 2019-12-31 | 2024-06-07 | 北京嘀嘀无限科技发展有限公司 | Method and device for determining travel time index, storage medium and electronic equipment |
| CN111581538B (en) * | 2020-04-10 | 2023-05-23 | 华南理工大学 | Expressway charging data-based expressway traffic flow state deducing method |
| CN111882858B (en) * | 2020-06-01 | 2022-05-20 | 重庆大学 | Multi-source data-based method for predicting queuing length of highway abnormal event |
| CN112434075B (en) * | 2020-10-23 | 2024-06-14 | 北京千方科技股份有限公司 | ETC portal-based traffic abnormality detection method and device, storage medium and terminal |
| CN112863172B (en) * | 2020-12-29 | 2022-10-28 | 千方捷通科技股份有限公司 | Highway traffic running state judgment method, early warning method, device and terminal |
| CN113380030A (en) * | 2021-06-03 | 2021-09-10 | 大连海事大学 | ETC technology-based urban traffic data acquisition method, system and storage medium |
| CN113870570B (en) * | 2021-12-02 | 2022-02-18 | 湖南省交通科学研究院有限公司 | ETC-based road network operation state method, system and storage medium |
| CN114898549B (en) * | 2022-03-07 | 2023-10-10 | 上海市城市建设设计研究总院（集团）有限公司 | Highway travel time information display system, method and information board |
| CN114999181B (en) * | 2022-05-11 | 2023-12-19 | 山东高速建设管理集团有限公司 | Highway vehicle speed abnormality identification method based on ETC system data |
| CN114944062B (en) * | 2022-05-30 | 2023-05-26 | 长安大学 | Construction method of tunnel parallel traffic system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081846A (en) * | 2011-02-22 | 2011-06-01 | 交通运输部公路科学研究所 | Expressway charge data track matching based traffic state recognition method |
| CN103258428A (en) * | 2013-04-24 | 2013-08-21 | 交通运输部公路科学研究所 | Method collecting traffic states based on ETC device |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000251194A (en) * | 1999-03-02 | 2000-09-14 | Toshiba Corp | Road travel supporting system |
| JP2005258726A (en) * | 2004-03-10 | 2005-09-22 | Mitsubishi Heavy Ind Ltd | System for providing road traffic information |
-
2014
- 2014-04-17 CN CN201410153549.4A patent/CN103886756B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081846A (en) * | 2011-02-22 | 2011-06-01 | 交通运输部公路科学研究所 | Expressway charge data track matching based traffic state recognition method |
| CN103258428A (en) * | 2013-04-24 | 2013-08-21 | 交通运输部公路科学研究所 | Method collecting traffic states based on ETC device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103886756A (en) | 2014-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103886756B (en) | Based on the freeway network method for detecting operation state of OBU | |
| CN108682173B (en) | Road traffic incident detection early warning method and system | |
| CN103258430B (en) | Road traveling time calculating and traffic road condition judging method and road traveling time calculating and traffic road condition judging device | |
| Ma et al. | Traffic demand estimation for lane groups at signal‐controlled intersections using travel times from video‐imaging detectors | |
| CN104464295B (en) | A kind of overhead Entrance ramp intelligence restricted driving method based on video | |
| CN102855760B (en) | On-line queuing length detection method based on floating vehicle data | |
| Brockfeld et al. | Benefits and limits of recent floating car data technology–an evaluation study | |
| CN104318770A (en) | Method for detecting traffic jam state of expressway in real time based on mobile phone data | |
| CN103077610A (en) | Road trip time estimating method and system | |
| CN109191861B (en) | System and method for detecting abnormal behavior of fee evasion vehicle on expressway based on video detector | |
| CN101739824A (en) | Data fusion technology-based traffic condition estimation method | |
| Liu et al. | Length-based vehicle classification using event-based loop detector data | |
| CN111768619A (en) | Express way vehicle OD point determining method based on checkpoint data | |
| An et al. | Lane-based traffic arrival pattern estimation using license plate recognition data | |
| CN109785627A (en) | A kind of crossroad access flux monitoring system | |
| CN109584560A (en) | A kind of traffic control adjusting method and system based on freeway traffic detection | |
| Gore et al. | Exploring credentials of Wi‐Fi sensors as a complementary transport data: an Indian experience | |
| CN104123837A (en) | Interrupted flow travel time estimation method based on microwave and video data fusion | |
| Huang et al. | Real time delay estimation for signalized intersection using transit vehicle positioning data | |
| Carli et al. | Monitoring traffic congestion in urban areas through probe vehicles: A case study analysis | |
| Peng et al. | Evaluation of roadway spatial-temporal travel speed estimation using mapped low-frequency AVL probe data | |
| Margreiter | Fast and reliable determination of the traffic state using Bluetooth detection on German freeways | |
| KR101035122B1 (en) | Measurement Method of Travel Time Using Sequence Pattern of Vehicles | |
| CN117173899A (en) | Smart city data processing method | |
| CN104952251A (en) | Urban viaduct traffic state sensing method based on bayonet and HADOOP technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20151230 |