CN112580954B - Simulation model calibration method based on traffic conflict extreme value distribution - Google Patents

Simulation model calibration method based on traffic conflict extreme value distribution Download PDF

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CN112580954B
CN112580954B CN202011460533.XA CN202011460533A CN112580954B CN 112580954 B CN112580954 B CN 112580954B CN 202011460533 A CN202011460533 A CN 202011460533A CN 112580954 B CN112580954 B CN 112580954B
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郭延永
刘攀
吴瑶
丁红亮
欧阳鹏瑛
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Abstract

The invention discloses a simulation model calibration method based on traffic conflict extreme value distribution, which comprises the following steps: (1) collecting traffic conflict data; (2) actually measured traffic conflict extreme value distribution fitting; (3) building a traffic simulation model; (4) and calibrating the traffic simulation model. The method comprises the steps of firstly collecting actual traffic conflict data, then fitting traffic conflict extreme value distribution, then building a traffic simulation model, and finally calibrating the simulation model based on the method provided by the invention. The invention can improve the accuracy of the traffic simulation model and has practical engineering application value in traffic safety evaluation.

Description

Simulation model calibration method based on traffic conflict extreme value distribution
Technical Field
The invention relates to the technical field of traffic safety and traffic simulation, in particular to a simulation model calibration method based on traffic conflict extreme value distribution.
Background
The occurrence frequency of traffic conflicts is high, and the method plays an important role in the evaluation of road traffic safety. However, the observation means of the traffic conflict is mainly a manual observation method, but the manual observation method has high cost, low repeatability and subjectivity to the judgment of the traffic conflict, and has inevitable defects. Particularly, when a traffic design scheme is evaluated, the scheme is in a design stage and does not fall to the ground, so that the traffic conflict data cannot be acquired by utilizing a manual observation method, and a traffic simulation method can provide a simulated traffic conflict for traffic safety evaluation. The existing traffic simulation conflict generally adopts default simulation model parameters, but the deviation of a simulation result from an actual measurement result is large, so that a large error of traffic safety evaluation is caused, and even an error of an evaluation result is caused.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a simulation model calibration method based on traffic conflict extreme value distribution, which can greatly improve the traffic conflict simulation result, reduce the influence of artificial subjective consciousness and obtain an accurate and reliable traffic safety evaluation result.
The invention adopts the following technical scheme for solving the technical problems: a simulation model calibration method based on traffic conflict extreme value distribution comprises the following steps:
the method comprises the following steps of 1, collecting traffic data, recording video at a signalized intersection by a camera, and collecting data of hourly traffic volume Q, average speed V, traffic flow running path L, signal timing W, traffic conflicts in unit time period of t minutes and collision time TTC of each traffic conflict through recorded video;
step 2, fitting the distribution of the actually measured traffic conflict extreme values, and fitting the distribution of the actually measured traffic conflict extreme values in the time period of t minutes by adopting a formula (1):
Figure GDA0003116737200000011
wherein y represents the collision time corresponding to the measured traffic collision for the time period of t minutes, F1(y) represents the distribution density function of the actually measured traffic conflict extremum, a1Position parameters representing the distribution of the extreme values of the actually measured traffic conflicts, b1A scale parameter representing the distribution of the extreme values of the actually measured traffic conflicts, c1Representing shape parameters of distribution of actually measured traffic conflict extreme values;
step 3, building a traffic simulation model, namely building a signalized intersection simulation model by adopting VISSIM (virtual visual identification system) simulation software, and inputting the actually measured hourly traffic quantity Q, the average speed V, the traffic flow driving path L and the signal timing W of the motor vehicle into the traffic simulation model; determining initial values of six parameters of a static distance, a safe distance, a vehicle headway, an expected acceleration, a safe distance reduction coefficient and a stop line distance in a traffic simulation model and setting a step length;
step 4, calibrating a traffic simulation model, starting to run traffic simulation, extracting traffic conflicts and collision time TTC' of each conflict when running traffic simulation each time, and fitting the simulated traffic conflict extreme value distribution in the time period of t minutes by adopting a formula (2):
Figure GDA0003116737200000021
wherein y 'represents the inverse number of collision time-TTC' corresponding to the simulated traffic collision in the time period of t minutes, and F2(y') represents the distribution density function of the extreme values of the simulated traffic conflicts, a2Position parameters representing the distribution of extreme values of simulated traffic conflicts, b2A scale parameter representing the distribution of extreme values of simulated traffic conflicts, c2A shape parameter representing a distribution of simulated traffic conflict extremes;
and (3) adopting a formula (3) to judge the simulation iteration convergence:
Figure GDA0003116737200000022
wherein A represents an iterative convergence index and alpha represents a weight;
let the iteration convergence index threshold be AthWhen A is less than or equal to AthAnd time, simulation iteration convergence, and traffic simulation parameters: the values corresponding to the static distance, the safe distance, the time interval of the locomotive, the expected acceleration, the safe distance reduction coefficient and the distance of the stop line are the values calibrated by the simulation model; a. the>AthThe simulation is continued until the iteration converges.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the invention provides a calibration method of a traffic simulation model, aiming at the simulation of traffic conflict extreme value distribution, the traffic conflict simulation result can be greatly improved, the artificial subjective consciousness influence is greatly reduced, and the accurate and reliable traffic safety evaluation result can be obtained.
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FIG. 1 is a method flow diagram of one embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The invention relates to a simulation model calibration method based on traffic conflict extreme value distribution, which comprises the following steps:
the method comprises the following steps of 1, collecting traffic data, recording at a signalized intersection by a camera, and collecting data of hourly traffic volume Q, average speed V, traffic flow running path L, signal timing W, traffic conflicts in a unit time period of 5 minutes and collision time TTC of each traffic conflict through recorded videos.
Step 2, fitting the distribution of the actually measured traffic conflict extreme values, wherein the actually measured traffic conflict extreme value distribution in the 5-minute time period is fitted by adopting a formula (1):
Figure GDA0003116737200000023
wherein y represents the inverse number of the collision time-TTC, F corresponding to the measured traffic collision in the 5 minute time period1(y) represents the distribution density function of the actually measured traffic conflict extremum, a1Position parameters representing the distribution of the extreme values of the actually measured traffic conflicts, b1A scale parameter representing the distribution of the extreme values of the actually measured traffic conflicts, c1And the shape parameter represents the distribution of the actually measured traffic conflict extreme values.
And 3, building a traffic simulation model, building a signalized intersection simulation model by adopting VISSIM simulation software, and inputting the actually measured hourly traffic quantity Q of the motor vehicle, the average speed V, the traffic flow driving path L and the signal timing W into the traffic simulation model. Six parameters of a static distance (stand-d, an initial value of 1m), a safe distance (safe-d, an initial value of 1m), a vehicle head time distance (ht, an initial value of 10s), an expected acceleration (da, an initial value of 0.5m/s2), a safe distance reduction coefficient (safe-f, an initial value of 0.5) and a stop line distance (stop-d, an initial value of 1m) in the traffic simulation model are set by 0.5 bit step length on the basis of the initial values.
Step 4, calibrating a traffic simulation model, starting to run traffic simulation, extracting traffic conflicts of each run traffic simulation and collision time of each conflict, and fitting the simulated traffic conflict extreme value distribution in the 5-minute time period by adopting a formula (2):
Figure GDA0003116737200000031
wherein y 'represents the inverse of the time to collision-TTC', F corresponding to the simulated traffic collision in the 5 minute time period2(y') represents the distribution density function of the extreme values of the simulated traffic conflicts, a2Position parameters representing the distribution of extreme values of simulated traffic conflicts, b2A scale parameter representing the distribution of extreme values of simulated traffic conflicts, c2And the shape parameter represents the distribution of the extreme values of the simulated traffic conflicts.
And (3) adopting a formula (3) to judge the simulation iteration convergence:
Figure GDA0003116737200000032
wherein A represents an iteration convergence index, which is recommended to be 0.5; α represents a weight, and is 0.3 in the present invention.
When A is less than or equal to 0.5, the simulation iteration is converged, and the values corresponding to the traffic simulation parameters stand-d, safe-d, ht, da, safe-f and stop-d are the values calibrated by the simulation model; when A >0.5, the simulation is continued until the iteration converges.
The present invention will be described with reference to specific examples.
1) Traffic data collection, as shown in table 1.
TABLE 1 traffic data Collection
Time interval (minutes) Q V L W T
5 Q1 V1 L1 W1 TTC11、TTC12、TTC13、…
10 Q2 V2 L2 W2 TTC21、TTC22、TTC23、…
15 Q3 V3 L3 W3 TTC31、TTC32、TTC33、…
… … … … … …
n QN VN LN WN TTCn1、TTCn2、TTCn3、…
2) Actually measured traffic conflict extreme value distribution fitting:
fitting the traffic conflict extreme value distribution according to the actual measurement data in the step 1), and respectively obtaining a1，b1，c1And the like.
Figure GDA0003116737200000033
3) Traffic simulation model building
Inputting the measured hourly traffic volume Q, the average speed V, the traffic flow driving path L and the signal timing W of the motor vehicle into traffic simulation VISSIM software, and inputting the static distance (stand-d, an initial value of 1m), the safe distance (safe-d, an initial value of 1m), the headway time (ht, an initial value of 10s), the expected acceleration (da, an initial value of 0.5 m/s) in a traffic simulation model2) The six parameters of safe distance reduction coefficient (safe-f, initial value 0.5) and stop line distance (stop-d, initial value 1m) are set with 0.5 bit step length on the basis of the initial values.
4) Traffic simulation model calibration
Fitting the simulated traffic conflict extreme distribution in the 5-minute time period based on the traffic conflict of each running traffic simulation and the collision time of each conflict,
Figure GDA0003116737200000041
and determining the parameters of the final traffic simulation model based on the simulation iterative convergence function, wherein the experiment is performed under the assumed data, the simulation iterative convergence function A is assumed to be less than 0.5 in the 100 th iteration, and the corresponding values of the traffic simulation parameters of stand-d, safe-d, ht, da, safe-f and stop-d at the moment are determined as the calibration values of the final simulation model.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (1)

1. A simulation model calibration method based on traffic conflict extreme value distribution is characterized in that: the method comprises the following steps:
the method comprises the following steps of 1, collecting traffic data, recording video at a signalized intersection by a camera, and collecting data of hourly traffic volume Q, average speed V, traffic flow running path L, signal timing W, traffic conflicts in unit time period of t minutes and collision time TTC of each traffic conflict through recorded video;
step 2, fitting the distribution of the actually measured traffic conflict extreme values, and fitting the distribution of the actually measured traffic conflict extreme values in the time period of t minutes by adopting a formula (1):
Figure FDA0003116737190000011
wherein y represents the inverse number of the collision time-TTC, F corresponding to the measured traffic collision in the time period of t minutes1(y) represents the distribution density function of the actually measured traffic conflict extremum, a1Position parameters representing the distribution of the extreme values of the actually measured traffic conflicts, b1A scale parameter representing the distribution of the extreme values of the actually measured traffic conflicts, c1Representing shape parameters of distribution of actually measured traffic conflict extreme values;
step 3, building a traffic simulation model, namely building a signalized intersection simulation model by adopting VISSIM (virtual visual identification system) simulation software, and inputting the actually measured hourly traffic quantity Q, the average speed V, the traffic flow driving path L and the signal timing W of the motor vehicle into the traffic simulation model; determining initial values of six parameters of a static distance, a safe distance, a vehicle headway, an expected acceleration, a safe distance reduction coefficient and a stop line distance in a traffic simulation model and setting a step length;
step 4, calibrating a traffic simulation model, starting to run traffic simulation, extracting traffic conflicts and collision time TTC' of each conflict when running traffic simulation each time, and fitting the simulated traffic conflict extreme value distribution in the time period of t minutes by adopting a formula (2):
Figure FDA0003116737190000012
wherein y 'represents the inverse number of collision time-TTC' corresponding to the simulated traffic collision in the time period of t minutes, and F2(y') represents the distribution density function of the extreme values of the simulated traffic conflicts, a2Position parameters representing the distribution of extreme values of simulated traffic conflicts, b2A scale parameter representing the distribution of extreme values of simulated traffic conflicts, c2A shape parameter representing a distribution of simulated traffic conflict extremes;
and (3) adopting a formula (3) to judge the simulation iteration convergence:
Figure FDA0003116737190000013
wherein A represents an iterative convergence index and alpha represents a weight;
let the iteration convergence index threshold be AthWhen A is less than or equal to AthAnd time, simulation iteration convergence, and traffic simulation parameters: the values corresponding to the static distance, the safe distance, the time interval of the locomotive, the expected acceleration, the safe distance reduction coefficient and the distance of the stop line are the values calibrated by the simulation model; a. the>AthThe simulation is continued until the iteration converges.
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