CN111275964B - Road section correlation matrix calculation method based on checkpoint data - Google Patents

Road section correlation matrix calculation method based on checkpoint data Download PDF

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CN111275964B
CN111275964B CN202010039533.6A CN202010039533A CN111275964B CN 111275964 B CN111275964 B CN 111275964B CN 202010039533 A CN202010039533 A CN 202010039533A CN 111275964 B CN111275964 B CN 111275964B
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road
road section
calculating
vehicle
data
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CN111275964A (en
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杨霖
王晨
钟会玲
李鹏鹏
徐梦
沈斌
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Zhejiang Supcon Information Industry Co Ltd
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    • 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

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

Road section correlation matrix calculation method based on checkpoint data
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,...,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 (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 bayonetsab；
Wherein a and b represent road section numbers, a is a road section which is passed by the vehicle firstly, and b is a road section which is passed by the vehicle later;
(1-8) obtaining a correlation matrix of the road section
Figure GDA0002859927450000021
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
Vj＝(lj,sj,rj)
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:
Figure GDA0002859927450000022
wherein
Figure GDA0002859927450000023
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
Figure GDA0002859927450000024
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
Figure GDA0002859927450000031
Preferably, (1-6) comprises the steps of:
Figure GDA0002859927450000032
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
Figure GDA0002859927450000045
Wherein lj,sj,rjLeft turn, straight going and at time t for road segment j respectivelyTraffic flow in right turn, /)j,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；
Figure GDA0002859927450000041
Wherein the content of the first and second substances,
Figure GDA0002859927450000042
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 bayonetsab；
Figure GDA0002859927450000043
Wherein, VaRepresenting the flow of the section a, vabRepresenting the flow from the section a to the section b, rab≠rba。
(1-8) obtaining a correlation matrix of the road section
Figure GDA0002859927450000044
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
Figure GDA0002859927450000051
Wherein
Figure GDA0002859927450000052
a must precede b
Figure GDA0002859927450000053
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
Figure GDA0002859927450000054
7. Calculating the correlation between the road segments according to the following formula
Figure GDA0002859927450000055
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
Figure GDA0002859927450000056
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
Figure GDA0002859927450000061
P (r) is the probability distribution in the historical data,
calculating P (congestion | R) from the above formula yields the probability of congestion 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 bayonetsab；
Wherein a and b represent road section numbers, a is a road section which is passed by the vehicle firstly, and b is a road section which is passed by the vehicle later;
(1-8) obtaining a correlation matrix of the road section
Figure FDA0002855172960000011
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
Vj＝(lj,sj,rj)
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:
Figure FDA0002855172960000021
wherein
Figure FDA0002855172960000022
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
Figure FDA0002855172960000023
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:
Figure FDA0002855172960000024
wherein, VaRepresenting the flow of the section a, vabRepresenting the flow from the section a to the section b, rab≠rba。
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