CN111275964A - Road section correlation matrix calculation method based on checkpoint data - Google Patents
Road section correlation matrix calculation method based on checkpoint data Download PDFInfo
- Publication number
- CN111275964A CN111275964A CN202010039533.6A CN202010039533A CN111275964A CN 111275964 A CN111275964 A CN 111275964A CN 202010039533 A CN202010039533 A CN 202010039533A CN 111275964 A CN111275964 A CN 111275964A
- Authority
- CN
- China
- Prior art keywords
- road
- road section
- calculating
- vehicle
- data
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Mathematical Physics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computing Systems (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a method for calculating a road section correlation matrix based on checkpoint data, which comprises the following steps: collecting the gate data of all gates of all road sections, wherein the gate data records the license plate number and the passing time of vehicles passing through the road sections; splitting each road section according to a splitting rule that the road section provided with the bidirectional lane is split into two road sections, and the road section provided with the unidirectional lane is kept unchanged; calculating the driving path of each vehicle by using the checkpoint data; the method has the characteristics that the relation between road sections can be more clearly known through the calculation of the road section correlation, and the method has guiding effects on the congestion analysis, the control of traffic lights and the arrangement of tidal lanes.
Description
Technical Field
The invention relates to the technical field of traffic simulation visualization, traffic optimization, congestion analysis and traffic situation analysis, in particular to a method for calculating a road segment correlation matrix based on checkpoint data.
Background
With the rapid development of economy, the problem of traffic congestion has spread to all levels of cities in recent years, and is not limited to big cities any more, the travel cost caused by traffic congestion is increased rapidly, a series of negative results such as increase of commuting time, energy waste and environmental pollution appear, and the problem becomes one of the main problems to be solved urgently by municipal departments.
In urban roads, the treatment of traffic jam is mainly divided into a software layer and a hardware layer, wherein the hardware layer is realized by widening roads, improving road topological structures and building intelligent equipment such as signal lamps and cameras, and the software layer is realized by optimizing traffic signal lamps through data acquired by the equipment of the cameras and timing tidal lanes; and the traffic signal lamp is optimized, and the tidal lane timing lamp depends on the characteristics of traffic flow data and the characteristic relation of roads.
Therefore, the research on the road correlation can dredge the traffic for traffic managers and provide reliable theoretical support for the optimization of signal timing and tide lane timing.
In the current research on timing of traffic lights and tide lanes, a camera device is mainly used for shooting road sections, calculating the queuing length and then making a corresponding timing scheme; in the aspect of research on road section correlation, some scholars adopt a principal component analysis method, the method cannot accurately reflect the real correlation of the road section, so that the tidal lane timing and signal lamp timing of the road section are delayed, and the traffic jam cannot be well solved.
Disclosure of Invention
The invention aims to overcome the defects that the tidal lane timing and signal lamp timing delay of a road section are caused by the fact that a principal component analysis method in the prior art cannot accurately reflect the real correlation of the road section, and provides a method for calculating a road section correlation matrix based on checkpoint data.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for calculating a road section correlation matrix based on bayonet data comprises the following steps: (1-1) collecting the gate data of all gates of all road sections, wherein the gate data records the license plate number and the passing time of vehicles passing through the road sections;
(1-2) splitting each road section, wherein the splitting rule is that the road section provided with the bidirectional lane is split into two road sections, and the road section provided with the unidirectional lane is kept unchanged;
(1-3) calculating a driving path of each vehicle by using the checkpoint data;
Ci＝(k1，k2，k3，...，kn)，
the corresponding time series is:
Ti＝(t1，t2，t3，...，tn)，
wherein k is1，k2，k3，...，knNumber the passing gate of vehicle, t1，t2，t3，...，tn is the respective passing k of the vehicle1，k2，k3，...，knThe time of the gate, i, represents the vehicle number;
(1-4) calculating the traffic flow of left turn, straight run and right turn of each traffic road section according to the time stamp of the driving track (taking five minutes as a statistical period);
(1-5) utilizing C of each vehicleiCalculating the flow v between the bayonetsab；
(1-6) calculating the correlation r between intersectionsab；
(1-8) obtaining a correlation matrix of the road section
Wherein, each element value on a diagonal line formed by two elements with equal subscripts of the correlation matrix is equal to 0, and m is the total number of road sections.
The invention provides an algorithm for calculating the intersection correlation based on bayonet data by researching the relationship between road sections, and the intersection correlation is calculated by the algorithm and is used as a reference index for timing of tidal lanes and signal lamp optimization.
Preferably, (1-4) comprises the steps of:
then t2Time t3The flow at that moment is denoted t3The flow rate at a moment;
the flow rate of the link j at the time t is
Wherein lj，sj，rjTraffic flows of left turn, straight run and right turn, l, respectively, of the link j at time tj，sj，rjIs the flow rate information counted from the traveling tracks of all the vehicles on the link j.
Preferably, (1-5) comprises the steps of:
wherein the content of the first and second substances,
the orderly containing means that a and b both represent road section numbers, a is not equal to b, a is a road section which is passed by a vehicle firstly, and b is a road section which is passed by the vehicle later; thus, it is possible to provide
Preferably, the correlation between adjacent road segments is calculated by the following formula:
the relevance of a left turn, a straight run and a right turn of a road segment j is equal to
Preferably, (1-6) comprises the steps of:
wherein, VaRepresenting the flow of the section a, vabRepresenting the flow from the section a to the section b, rab≠rba。
Therefore, the invention has the following beneficial effects: the concept of the relevance of the road sections is provided, the relation between the road sections is more clearly understood through the calculation of the relevance of the road sections, and the guidance function is provided for the congestion analysis, the control of traffic lights and the arrangement of tidal lanes; according to the relevance between the road sections in different time periods, the trend of the traffic flow among the road sections is excavated, and the occupational and residential area distribution of the city is excavated.
Drawings
FIG. 1 is a schematic illustration of a road segment split according to the present invention;
FIG. 2 is a thermodynamic diagram relating to a Kelvin bayonet of the present invention;
FIG. 3 is a thermodynamic diagram relating to another embodiment of the present invention;
FIG. 4 is a thermodynamic diagram relating to a third type of bridge bayonet of the present invention.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
A method for calculating a road section correlation matrix based on bayonet data comprises the following steps:
(1-1) collecting the gate data of all gates of all road sections, wherein the gate data records the license plate number and the passing time of vehicles passing through the road sections;
(1-2) splitting each road section, wherein the splitting rule is that the road section provided with the bidirectional lane is split into two road sections, and the road section provided with the unidirectional lane is kept unchanged;
(1-3) calculating a driving path of each vehicle by using the checkpoint data;
Ci＝(k1，k2，k3，...，kn)，
the corresponding time series is:
Ti＝(t1，t2，t3，...，tn)，
wherein k is1，k2，k3，...，knNumber the passing gate of vehicle, t1，t2，t3，...，tnFor the vehicle to pass respectively k1，k2，k3，...，knThe time of the gate, i, represents the vehicle number;
(1-4) calculating the traffic flow of left turn, straight run and right turn of each traffic road section according to the time stamp of the driving track;
then t2Time t3The flow at that moment is denoted t3The flow rate at a moment;
the flow rate of the link j at the time t is
Wherein lj，sj，rjTraffic flows of left turn, straight run and right turn, l, respectively, of the link j at time tj，sj，rjIs the flow rate information counted from the traveling tracks of all the vehicles on the link j.
(1-5) utilizing C of each vehicleiCalculating the flow v between the bayonetsab；
Wherein the content of the first and second substances,
the orderly containing means that a and b both represent road section numbers, a is not equal to b, a is a road section which is passed by a vehicle firstly, and b is a road section which is passed by the vehicle later.
(1-6) calculating the correlation r between intersectionsab；
Wherein, VaRepresenting the flow of the section a, vabTo representFlow from section a to section b, rab≠rba。
(1-8) obtaining a correlation matrix of the road section
Wherein, each element value on a diagonal line formed by two elements with equal subscripts of the correlation matrix is equal to 0, and m is the total number of road sections.
Example (c): taking shaoxing kokko bridge as an example, 1156847 pieces of data are selected from 7-8, 13-14 and 17-18 bayonet data of 5-14-year-round in the whole area 2019, and 264 bayonets and 184559 vehicles are covered;
the method is used for calculating the correlation among the roads in the coastal regions and comprises the following specific steps:
1. collecting checkpoint data of 5 months and 14 days in 2019 of the Kokko bridge, wherein the checkpoint data comprises the following information;
(1) PLATE _ NUMBER VEHICLE NUMBER
(2) Time elapsed for JGSJ vehicle
(3) ID card port numbering
2. Splitting the road section according to the graph shown in fig. 1, updating the information of the split road section to the data of the gate, and enabling the road section information to correspond to the gates, wherein one gate corresponds to one road section; the 4 road segments, RoadA, RoadB, RoadC and RoadD, are split into 6 road segments, a1, a2, B1, B2, RoadC and RoadD;
3. cleaning data, wherein the cleaning rule is as follows:
1) deleting the license plate data which cannot be identified;
2) only one piece of recorded vehicle data cannot form a track, and the data is deleted;
3) data of the same vehicle continuously passing through the same gate within 10 minutes (due to the fact that the vehicle stays at the gate shooting position for a long time, a plurality of data are uploaded at different time points of the same gate);
4. data pre-processing
1) Splitting two data of the same vehicle into two records with the time interval of more than one hour (so as to avoid too large delay of the calculated flow data);
5. generating a driving track from the checkpoint data of each vehicle
Ci＝(k1，k2，k3，...，kn)，
The corresponding time series is:
Ti＝(t1，t2，t3，...，tn)，
6. flow data between bayonets according to the following formula
Wherein
a must precede b
Wherein the correlation of adjacent road sections is calculated by the following formula:
the relevance of a left turn, a straight run and a right turn of a road segment j is equal to
7. Calculating the correlation between the road segments according to the following formula
Wherein VaRepresenting the flow of the section a, vabIndicating the flow from road segment a to road segment b.
Intersection dependencies do not have symmetry, i.e. Rab≠Rba
8. Obtaining a correlation matrix for road segments
Each element value on a diagonal line formed by two elements with the same subscript of the correlation matrix is equal to 0, and m is the total number of road sections;
9. based on the congestion analysis of the correlation matrix, by analyzing the bayonet with larger correlation in fig. 2, 3 and 4, the part with higher correlation is brighter in the graph, the correlation between the road sections in different time periods is different, the congestion condition of the road section in one year and the corresponding road section correlation can be counted in the specific implementation, and a congestion prior probability distribution is obtained for each road section;
p (R | Congestion)
Wherein R is the correlation of the road section, and the congestion of the road section is predicted according to a Bayesian formula
P (Congestion | R) ═ P (R | Congestion) P (Congestion)/P (R)
Wherein P (Rcongestion) is the calculated value P (congestion), and P (R) is the historical statistical result, specifically calculated as
P (r) is the probability distribution in the historical data,
p (congestion | R) is calculated from the above formula to obtain the congestion probability through the link.
It should be understood that this example is for illustrative purposes only and is not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
Claims (5)
1. A method for calculating a road section correlation matrix based on checkpoint data is characterized by comprising the following steps:
(1-1) collecting the gate data of all gates of all road sections, wherein the gate data records the license plate number and the passing time of vehicles passing through the road sections;
(1-2) splitting each road section, wherein the splitting rule is that the road section provided with the bidirectional lane is split into two road sections, and the road section provided with the unidirectional lane is kept unchanged;
(1-3) calculating a driving path of each vehicle by using the checkpoint data;
Ci＝(k1，k2，k3，...，kn)，
the corresponding time series is:
Ti＝(t1，t2，t3，...，tn)，
wherein k is1，k2，k3，...，knNumber the passing gate of vehicle, t1，t2，t3，...，tnFor the vehicle to pass respectively k1，k2，k3，...，knThe time of the gate, i, represents the vehicle number;
(1-4) calculating the traffic flow of left turn, straight run and right turn of each traffic road section according to the time stamp of the driving track;
(1-5) utilizing C of each vehicleiCalculating the flow v between the bayonetsab；
(1-6) calculating the correlation r between intersectionsab；
(1-8) obtaining a correlation matrix of the road section
Wherein, each element value on a diagonal line formed by two elements with equal subscripts of the correlation matrix is equal to 0, and m is the total number of road sections.
2. The method for calculating a road segment correlation matrix based on bayonet data as claimed in claim 1, wherein (1-4) comprises the steps of:
then t2Time t3The flow at that moment is denoted t3The flow rate at a moment;
the flow rate of the link j at the time t is
Wherein lj，sj，rjTraffic flows of left turn, straight run and right turn, l, respectively, of the link j at time tj，sj，rjIs the flow rate information counted from the traveling tracks of all the vehicles on the link j.
3. The method for calculating a road segment correlation matrix based on bayonet data as set forth in claim 1, wherein (1-5) comprises the steps of:
wherein the content of the first and second substances,
the orderly containing means that a and b both represent road section numbers, a is not equal to b, a is a road section which is passed by a vehicle firstly, and b is a road section which is passed by the vehicle later.
4. The method of claim 1, wherein the correlation between adjacent road segments is calculated by the following formula:
the relevance of a left turn, a straight run and a right turn of a road segment j is equal to
5. The method for calculating a road segment correlation matrix based on bayonet data as claimed in claim 1, wherein (1-6) comprises the steps of:
wherein, VaRepresenting the flow of the section a, vabRepresenting the flow from the section a to the section b, rab≠rba。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010039533.6A CN111275964B (en) | 2020-01-14 | 2020-01-14 | Road section correlation matrix calculation method based on checkpoint data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010039533.6A CN111275964B (en) | 2020-01-14 | 2020-01-14 | Road section correlation matrix calculation method based on checkpoint data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111275964A true CN111275964A (en) | 2020-06-12 |
| CN111275964B CN111275964B (en) | 2021-03-23 |
Family
ID=71001680
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010039533.6A Active CN111275964B (en) | 2020-01-14 | 2020-01-14 | Road section correlation matrix calculation method based on checkpoint data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111275964B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011004026A2 (en) * | 2009-07-09 | 2011-01-13 | Tomtom International Bv | Navigation devices and methods carried out thereon |
| CN102915637A (en) * | 2012-08-01 | 2013-02-06 | 浙江浙大中控信息技术有限公司 | Method and system for traffic state evaluation at signal control crossing |
| JP2013250688A (en) * | 2012-05-31 | 2013-12-12 | Denso Corp | Proximity notification device |
| CN109285346A (en) * | 2018-09-07 | 2019-01-29 | 北京航空航天大学 | A kind of city road net traffic state prediction technique based on key road segment |
| CN109410577A (en) * | 2018-11-09 | 2019-03-01 | 东北林业大学 | Adaptive traffic control sub-area division method based on Spatial Data Mining |
-
2020
- 2020-01-14 CN CN202010039533.6A patent/CN111275964B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011004026A2 (en) * | 2009-07-09 | 2011-01-13 | Tomtom International Bv | Navigation devices and methods carried out thereon |
| JP2013250688A (en) * | 2012-05-31 | 2013-12-12 | Denso Corp | Proximity notification device |
| CN102915637A (en) * | 2012-08-01 | 2013-02-06 | 浙江浙大中控信息技术有限公司 | Method and system for traffic state evaluation at signal control crossing |
| CN109285346A (en) * | 2018-09-07 | 2019-01-29 | 北京航空航天大学 | A kind of city road net traffic state prediction technique based on key road segment |
| CN109410577A (en) * | 2018-11-09 | 2019-03-01 | 东北林业大学 | Adaptive traffic control sub-area division method based on Spatial Data Mining |
Non-Patent Citations (2)
| Title |
|---|
| SOMWRITA SARKAR,SANJAY CHAWLA ; SHAMEEM AHMAD,JAIDEEP SRIVAS: "Effective Urban Structure Inference from Traffic Flow Dynamics", 《IEEE TRANSACTIONS ON BIG DATA》 * |
| 刘金龙: "基于多传感器信息融合的行驶工况判别技术研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111275964B (en) | 2021-03-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103903433B (en) | The Real-time and Dynamic method of discrimination of a kind of road traffic state and device | |
| WO2019047905A1 (en) | Road traffic analysis system, method and apparatus | |
| CN108335496B (en) | City-level traffic signal optimization method and system | |
| CN101930668A (en) | Road traffic OD (Optical Density) information collection system for license plate recognition and processing method thereof | |
| CN109345832B (en) | Urban road overtaking prediction method based on deep recurrent neural network | |
| CN107885795A (en) | A kind of data verification method, system and the device of bayonet socket data | |
| Lee | Modeling gap acceptance at freeway merges | |
| CN103295404A (en) | Road section pedestrian traffic signal control system based on pedestrian crossing clearance time | |
| CN111881557B (en) | Traffic flow simulation method based on average speed of road | |
| CN112863182A (en) | Cross-modal data prediction method based on transfer learning | |
| CN112967493A (en) | Neural network-based prediction method for vehicle passing intersection travel time | |
| CN111260522A (en) | Vehicle travel characteristic visualization analysis system based on big data | |
| Wei et al. | Turn-level network traffic bottleneck identification using vehicle trajectory data | |
| CN111275964B (en) | Road section correlation matrix calculation method based on checkpoint data | |
| Jintanakul et al. | Bayesian mixture model for estimating freeway travel time distributions from small probe samples from multiple days | |
| Eisele et al. | Developing and applying models for estimating arterial corridor travel time index for transportation planning in small to medium-sized communities | |
| Karimpour | Data-Driven Approaches for Assessing the Impact of Speed Management Strategies for Arterial Mobility and Safety | |
| Clara Fang et al. | An intelligent traffic signal control system based on fuzzy theory | |
| Liu | Bi-level optimization algorithm for dynamic reversible lane control based on short-term traffic flow prediction | |
| Li | High-resolution data-based methods for arterial traffic volume estimation | |
| CN105206054B (en) | Method for digging is repeated based on the floating wheel paths that big data is analyzed | |
| Mishraa et al. | Short term traffic prediction using Monte Carlo simulation | |
| Pulugurtha et al. | Comparative evaluation of Synchro and VISSIM traffic simulation software to model railroad crossings | |
| Ayele et al. | Validating the Interval Valued Neutrosophic Soft Set Traffic Signal Control Model Using Delay Simulation | |
| Adhikari | Implementation of Platoon Based Traffic Signal Control for Diamond Intersections: A Case Study in Houston |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 310053 23-25, 2 building, 352 BINKANG Road, Binjiang District, Hangzhou, Zhejiang. Patentee after: Zhejiang zhongkong Information Industry Co.,Ltd. Address before: 310053 23-25, 2 building, 352 BINKANG Road, Binjiang District, Hangzhou, Zhejiang. Patentee before: ZHEJIANG SUPCON INFORMATION TECHNOLOGY Co.,Ltd. |
|
| CP01 | Change in the name or title of a patent holder |