CN115862322A - Vehicle variable speed limit control optimization method, system, medium and equipment - Google Patents
Vehicle variable speed limit control optimization method, system, medium and equipment Download PDFInfo
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Abstract
The invention provides a method, a system, a medium and equipment for optimizing variable speed limit control of a vehicle, relating to the technical field of vehicle control and comprising the steps of determining a path to be implemented by the variable speed limit control, selecting a target road section and acquiring traffic flow data of the target road section; constructing a real road network model of a target road section, and carrying the real road network model into the road network model according to the position information of the real electromechanical equipment of the road; calibrating the road network model according to the traffic flow data of the target road section, and simulating the condition that the number of main road lanes is reduced due to accidents of all sub-road sections under different service levels by using the road network model; and constructing a variable speed-limiting dual-target optimization model, transmitting the simulated traffic flow data under each condition to the variable speed-limiting dual-target optimization model, and solving a corresponding variable speed-limiting control strategy by using an NSGA-II algorithm. The safety and efficiency of the road section are optimized and improved through variable speed limit control.
Description
Technical Field
The disclosure relates to the technical field of vehicle control, in particular to a method, a system, a medium and equipment for optimizing variable speed limit control of a vehicle.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Variable Speed Limit (VSL) is one of traffic control measures, and means that active intervention is performed on a road traffic flow on an expressway or an urban expressway according to factors such as traffic conditions, weather conditions, traffic accidents and the like, and a Speed Limit value of a road section is dynamically adjusted, so that the purposes of improving the traffic flow, relieving traffic congestion, improving road safety and the like are achieved.
The research scenes of the variable speed limit control generally comprise severe weather such as rain and fog, traffic accidents in front, road construction, congestion caused by ramp influence and the like, when the number of lanes on a main road is reduced, the congestion is caused by the influence of entrance and exit ramps and under the condition of flow increase, the safety and the efficiency of a road section are optimized and improved by the variable speed limit control. Because the variable speed limit control strategy technology is difficult to test in an actual scene, related researches are mostly carried out by combining a simulation model. In the variable speed limit research aiming at improving the traffic efficiency, a macroscopic simulation model is mostly adopted for analysis, and the macroscopic simulation model mainly comprises a METANET model and a CTM model; in the variable speed limit research aiming at improving the road safety, micro simulation models such as PARAMICS, VISSIM and the like are used more frequently. Macroscopic models such as MEATNET and CTM cannot analyze the running characteristics of a single vehicle, cannot describe behaviors such as acceleration, deceleration and speed limit compliance of a driver, and cannot directly calculate and describe safety indexes; the microscopic models such as PARAMICs and VISSIM can overcome the defects of the macroscopic model, analyze the traffic flow from the perspective of a single vehicle, but have less related research on simulation optimization by the microscopic simulation model. Meanwhile, the existing research mostly uses a single optimization target as an influencing factor of strategy selection, and the research on the variable speed limit strategy of multi-target optimization is less.
Disclosure of Invention
The invention provides a method, a system, a medium and equipment for controlling and optimizing variable speed limit of a vehicle, which aim to optimize road traffic efficiency and traffic safety, combine VISSIM micro simulation traffic flow state, and adopt NSGA-II multi-target optimization algorithm to solve the multi-stage variable speed limit control strategy of generating component time intervals and sub-segments.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a variable speed limit control optimization method for a vehicle comprises the following steps:
determining a path to be implemented by variable speed limit control, selecting a target road section, and acquiring traffic flow data of the target road section;
constructing a real road network model of a target road section, and carrying the real road network model into the road network model according to the position information of the real electromechanical equipment of the road;
calibrating a road network model according to the traffic flow data of the target road section, and simulating the condition that the number of lanes of a main road is reduced due to accidents of each sub-road section under different service levels by using the road network model;
and constructing a variable speed limit dual-target optimization model, transmitting the simulated traffic flow data under each condition to the variable speed limit dual-target optimization model, and solving a corresponding variable speed limit control strategy by using an NSGA-II algorithm.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a variable speed limit control optimization system for a vehicle, comprising:
the data initialization module is used for determining a path to be implemented by variable speed limit control, selecting a target road section and acquiring traffic flow data of the target road section;
the model building module is used for building a real road network model of a target road section and carrying the real road network model into the road network model according to the position information of the real electromechanical equipment of the road;
the strategy solving module is used for calibrating the road network model according to the traffic flow data of the target road section and simulating the condition that the number of main road lanes is reduced due to accidents of each sub-road section under different service levels by using the road network model; and constructing a variable speed-limiting dual-target optimization model, transmitting the simulated traffic flow data under each condition to the variable speed-limiting dual-target optimization model, and solving a corresponding variable speed-limiting control strategy by using an NSGA-II algorithm.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute a method of optimizing variable speed limit control for a vehicle.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the vehicle variable speed limit control optimization method.
Compared with the prior art, the beneficial effect of this disclosure is:
the method provided by the disclosure simulates a target road section, carries out simulation optimization by a microscopic simulation model, adopts an NSGA-II multi-objective optimization algorithm to solve a multi-stage variable speed limit control strategy for generating component time intervals and sub-road sections, reduces the number of lanes on a main road, is influenced by an entrance ramp and an exit ramp and congestion formed under the condition of flow increase when the problems of severe weather such as rain, fog and the like, traffic accidents in the front, road construction, congestion caused by the influence of ramps and the like occur, optimizes and promotes the safety and efficiency of the road section through variable speed limit control, relieves the congestion condition of a congestion area, improves the overall traffic efficiency, and promotes the road safety.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a schematic diagram illustrating an implementation flow of a variable speed limit control strategy in an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of a road network simulation model and optimization algorithm combination method in the embodiment of the present disclosure;
FIG. 3 is a schematic diagram of fast non-dominated sorting in an embodiment of the disclosure;
fig. 4 is a schematic diagram of congestion calculation in an embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating the selection of the NSGA-II algorithm in an embodiment of the present disclosure;
FIG. 6 is a flow chart of the computation of the NSGA-II algorithm in an embodiment of the present disclosure.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The research scenes of the variable speed limit control generally comprise severe weather such as rain and fog, traffic accidents in the front, road construction, congestion caused by ramp influence and the like.
Example 1
An embodiment of the present disclosure provides a method for optimizing variable speed limit control of a vehicle, including:
step 1: determining a path to be implemented by variable speed limit control, selecting a target road section, and acquiring traffic flow data of the target road section;
step 2: constructing a real road network model of a target road section, and carrying the real road network model into the road network model according to the position information of the real electromechanical equipment of the road;
and 3, step 3: calibrating a road network model according to the traffic flow data of the target road section, and simulating the condition that the number of lanes of a main road is reduced due to accidents of each sub-road section under different service levels by using the road network model;
and 4, step 4: and constructing a variable speed-limiting dual-target optimization model, transmitting the simulated traffic flow data under each condition to the variable speed-limiting dual-target optimization model, and solving a corresponding variable speed-limiting control strategy by using an NSGA-II algorithm.
The key elements of the variable speed limit control comprise the arrangement position selection of the speed limit sign, the length of the control period of the speed limit, the magnitude of the variation amplitude and the like, and different values have influence on the control effect of the variable speed limit, so that the variable speed limit control method is taken as the following control limit:
(1) The distance range between the starting point of the bottleneck section and the upstream speed-limiting control section is 500-700 m;
(2) Determining that the distance between the two control sections should be kept at least 1.5km;
(3) The control period is suitably 5 to 10 minutes, and in the invention, the control period is 10 minutes.
(4) The speed limit range is determined according to the maximum and minimum speed limit of the road section, and the speed limit value is generally a multiple of 10;
(5) The maximum variation amplitude of the speed limit is usually 20km/h or 10km/h, and the maximum variation amplitude is 10km/h in the disclosure.
As an example, in step 1, a link having a variable speed limit control policy implementation condition is determined according to the variable speed limit key element setting.
In step 2, a real road network model of the target road section is constructed, and the road network model is loaded according to the position information of the real electromechanical equipment of the road; specifically, the position information of the electromechanical devices such as a road real detector and a variable information board is loaded into the road network simulation model.
Because macroscopic traffic flow models such as METANET and CTM are difficult to provide information such as the position and the speed of each vehicle in a road section, microscopic simulation software VISSIM is selected to build and calculate a road network simulation model.
In step 3, calibrating the road network model according to the traffic flow data of the target road section, and simulating the situation that the number of main road lanes is reduced due to accidents of each sub-road section under different service levels by using the road network model, specifically comprises the following steps: the position of the variable information board is used as a speed-limiting control section, sub-road sections are divided among the sections, traffic running conditions are divided according to service levels, and a road network model generated by VISSIM is used for simulating the condition that the number of main road lanes is reduced due to accidents of each sub-road section under different service levels.
The road network model is calibrated by adopting road section real traffic flow data in the following steps:
(1) Selecting a calibration index at least comprising traffic volume or traffic capacity, and selecting a system operation index as a condition for judging the stop of calibration;
(2) Selecting a driving behavior model parameter as a parameter to be calibrated;
(3) Carrying out parameter calibration by adopting a test optimization method and a heuristic algorithm;
(4) And carrying out reliability test on the model by using the actual data through a statistical verification method.
As an embodiment, in step 4, a variable speed-limiting dual-target optimization model is constructed, the simulated traffic flow data under each condition is transmitted to the variable speed-limiting dual-target optimization model, and a corresponding variable speed-limiting control strategy is solved by using the NSGA-ii algorithm.
Firstly, a variable speed-limiting double-target optimization model is constructed, and the variable speed-limiting double-target optimization model selects an NSGA-II algorithm in a multi-target genetic algorithm to optimize by taking traffic efficiency and road safety as targets. Because two objective functions of safety and efficiency are considered at the same time, an intelligent optimization algorithm is selected for solving, and an NSGA-II algorithm in the multi-objective genetic algorithm is selected as the optimization algorithm. According to the concept of simulation optimization SBO, the output value of a simulation model is used as an adaptive value of an optimization algorithm, and the solution thought for constructing a variable speed limit dual-target optimization model is shown in the attached figure 2, namely: firstly, establishing a basic VISSIM simulation model, and establishing an interface between the simulation model and an optimization algorithm through Python secondary development; then, possible speed limit strategies are generated by an NSGA-II algorithm and input to the VISSIM simulation model, an objective function corresponding to each speed limit strategy is obtained through calculation of the intersection simulation model, the NSGA-II obtains the objective function value, evolution iteration is carried out, and a new variable speed limit strategy is searched until evolution is completed; and finally, obtaining an optimal variable speed limit control strategy set.
Non-dominant Genetic Algorithms (NSGA) are extensions of Genetic Algorithms based on Pareto's optimal concept, with individuals layered according to a dominant relationship. After the NSGA algorithm is improved, an NSGA-II algorithm which is lower in computational complexity, guarantees population diversity and keeps high-quality individuals is obtained.
An objective function is constructed by two optimization targets of traffic efficiency and road safety, wherein the average travel time is used as a traffic efficiency index, the collision probability is used as a road safety index, and the constraint condition is determined to be that the difference between speed limit values given in adjacent control periods is less than or equal to 10km/h on the same control section, and the difference between speed limit values on adjacent control sections is less than or equal to 10km/h in the same control period.
The specific implementation process is as follows:
in order to relieve the congestion condition of the congestion area, improve the overall traffic efficiency and improve the road safety, the invention selects two targets of efficiency and safety to optimize.
(1) Target function based on traffic efficiency improvement
The Average Travel Time (ATT) is the Average Time taken by the vehicle to Travel through the road section, the Total Travel Time (TTT) is the Total Time taken by all vehicles traveling through the road section from the start point to the end point, and the relation between the TTT and the ATT is shown in formula (1).
In the formula: n is the total number of vehicles.
The decrease in total and average travel time represents an increase in overall efficiency of the road segment, with a smaller average travel time representing less time spent by each driver through congested road segments.
(2) Objective function based on road safety improvement
The alternative safety index may be used To analyze a vehicle Collision situation in a road To achieve evaluation of road safety, and therefore, a Time To Collision (TTC) in the alternative safety index is used as a safety index, which is defined as: if the driving state of the front vehicle and the rear vehicle is kept unchanged, the time required by the rear vehicle to collide with the front vehicle is taken, and if appropriate preventive measures such as deceleration of the rear vehicle and the like can be taken in the time interval, the collision can be avoided, and the calculation formula is shown as the formula (2).
In the formula: t is the time interval, i is the vehicle number, vehicle i +1 represents the vehicle following vehicle i, TTC i,t Indicating the time of collision, x, of the vehicle i at time t i (t) represents the position of the vehicle i at time t, v i (t) represents the speed of the vehicle i at time t.
Bachmann improves equation (2), divides the speed relationship when the vehicle follows, and the improved collision time equation is shown in equation (3), and when the speed of the rear vehicle is less than or equal to the speed of the front vehicle, the collision time is set to infinity.
The collision time can be used to calculate the collision probability of the whole road section in the time period, and the calculation formula is shown as formula (4), which means: and setting a collision time threshold, wherein the proportion of the number of collisions in the road section which are lower than the collision time threshold to all the number of collisions is the collision probability. According to general provisions, when the calculated collision time is less than 1.5s, the corresponding collision should be defined as a severe collision, so the threshold value of the collision time in equation (4) takes 1.5s.
In the formula: f CL To the collision probability, n CL Indicating a number of collisions less than a collision time threshold, n TTC Indicating the total number of collisions.
(3) Two-objective optimization function
A method based on an intelligent optimization algorithm is used for simultaneously optimizing two targets of safety and efficiency to give a Pareto optimal solution of safety and efficiency, and an objective function is shown as a formula (5).
In addition, as for the constraint conditions,
in connection with a maximum variation amplitude of 10km/h, it should be determined that: on the same control section, the difference between the speed limit values given in adjacent control periods needs to be less than or equal to 10km/h; in the same control period, the difference between the speed limit values on adjacent control sections also needs to be less than or equal to 10km/h, as shown in formula (6).
In the formula: v. of vsl,l (k) And the speed limit value of the kth control period and the l control section is shown.
The position of the variable information board is used as a speed-limiting control section, sub-road sections are divided among the sections, and meanwhile, the traffic operation condition is divided according to the service level. And simulating the situation that the number of lanes of the main road is reduced due to accidents of each sub-road section under different service levels by using the VISSIM, transmitting the simulated traffic flow data to a multi-target optimization genetic algorithm through a COM (component object model) interface aiming at each situation, and solving to obtain a corresponding variable speed limit control strategy.
Compared with the common genetic algorithm, the key steps of the NSGA-II algorithm comprise the following three steps:
(1) Fast non-dominated sorting
All individuals a in the population are given a parameter n a And set S a The meaning of the parameters is: individual a will be covered by n in the population a Individual dominated, individual dominated by individual a constitutes S a . In the first step, all solutions in the population that cannot be dominated by any other solution, i.e. all n a Solution of =0, add set R 1 Is by R 1 The individual b in (1) dominates the constituent set S of the individual b Will S b N of medium number k k All subtract 1 if n k -1=0 and adds to set H. R is 1 The non-dominant ranking level a of all individuals in the set is the non-dominant ranking level a of the Pareto with the rank 1 rank Are all the same. In the second step, the ranking operation continues on the set H until each individual has obtained a rank.
(2) Congestion calculation
Degree of congestion a d The density values of individuals in the population are shown, and the crowdedness of the ith individual in the double target problem is the sum of the length and the width of a dotted line quadrangle composed of i-1 and i +1 shown in the attached figure 4.
(3) Selecting a rank
Through the above two steps, all individuals were assigned Pareto rating a rank And degree of congestion a d Defining partial order relation < > as shown in formula (7) n Taking an individual with a smaller grade when the Pareto grades are different; when the levels are the same, the individuals with higher crowdedness are selected.
Parent P t And the child Q t Merging, sorting and selecting according to the above steps, and selecting n from the merged population size The individuals form a new parent P t+1 The schematic diagram is shown in figure 5.
The overall algorithm flow is as follows: first, according to the size n of the population size Initializing a population, and after performing rapid non-dominated sorting on the obtained initial population, obtaining first generation filial generations according to basic operation of a genetic algorithm; updating the evolution times, adopting an elite strategy, performing rapid non-dominated sorting on the population of the merged parent and the offspring, and screening to obtain a new parent on the basis of calculating the congestion degree; until reaching the evolution times n gen And the evolution is completed. The algorithm flow chart is shown in fig. 6.
The combination algorithm of the NSGA-II algorithm aiming at the variable speed limit problem in the disclosure is as follows:
(1) Encoding
Assuming that the value of the speed limit value is an integer between 40 and 80, an integer code is adopted, one gene represents the speed limit value on one section at one moment, and a group of variable speed limit strategies form a possible solution. Taking the rate-limiting strategy of 4 control sections and 5 control cycles as an example, the length of the chromosome is 4 × 5, {80, 80, 70, 60;80 70, 70, 60;70 70, 70, 60;70 60, 60, 50;70 70, 70, 60, i.e., a set of possible solutions.
(2) Generating an initial population
Constraint conditions that the speed limit values of adjacent sections and adjacent time are less than or equal to 10km/h are required, and the constraint conditions are required to be met when an initial species group is generated: firstly, generating a speed limit value v of a first control section in a first control period vsl,1 (1) Randomly generating v under the condition of ensuring that the difference value is less than or equal to 10 vsl,1 (2)、v vsl,2 (1) Then, v is generated vsl,1 (3) And so on until the entire solution is generated. And repeatedly generating solution individuals until the requirement of the population scale is met.
(3) Crossover operator
Selecting single-point intersection as an intersection operator, namely: for a variable speed-limiting strategy false-a, another variable speed-limiting strategy false-b is selected as a parent, a point (m, n) is randomly selected, and the speed-limiting value of the variable speed-limiting strategy false-a backward from the point is replaced by the speed-limiting value of the same position of the strategy false-b. After the crossing is finished, the newly generated child-a is checked, and the constraint condition that the speed change difference between adjacent sections and adjacent time periods is less than 10 needs to be met; otherwise, the intersection is carried out again, and a child-a is generated again until the constraint condition is met.
In specific implementation, a plurality of simulated large-flow conditions are corresponding to corresponding variable speed-limiting strategies, and a variable speed-limiting control strategy library of a target road section is constructed.
The actual road running condition is judged by the detector, and the actual road running condition is input into the variable speed limit control strategy library for matching, so that a corresponding variable speed limit strategy is given, and the variable speed limit strategy is released and implemented by the variable information intelligence board.
The method is adopted to carry out simulation application on a section of urban expressway in Shanghai inner ring elevated road, and three variable speed-limiting strategies are compared and analyzed with the traffic state without variable speed limiting, and the result shows that: under the variable speed limit control strategy, the improvement of a safety index (-3.21%) and the reduction of the passing time (-6.41%) can be realized at the same time, the road safety (-26.47%) can be greatly improved on the premise of not influencing the passing efficiency (+ 0.87%), and when the safety index is improved more (-40.12%), a certain efficiency index (+ 15.78%) is sacrificed; the implementation effect is further verified by analyzing the average speed, the standard deviation of the speed and the contour map.
Example 2
In one embodiment of the present disclosure, a variable speed limit control optimization system for a vehicle is provided, including:
the data initialization module is used for determining a path to be implemented by variable speed limit control, selecting a target road section and acquiring traffic flow data of the target road section;
the model building module is used for building a real road network model of a target road section and carrying the real road network model into the road network model according to the position information of the real electromechanical equipment of the road;
the strategy solving module is used for calibrating the road network model according to the traffic flow data of the target road section and simulating the condition that the number of main road lanes is reduced due to accidents of each sub-road section under different service levels by using the road network model; and constructing a variable speed-limiting dual-target optimization model, transmitting the simulated traffic flow data under each condition to the variable speed-limiting dual-target optimization model, and solving a corresponding variable speed-limiting control strategy by using an NSGA-II algorithm.
Example 3
In one embodiment of the disclosure, a computer readable storage medium is provided, in which a plurality of instructions are stored, the instructions being adapted to be loaded by a processor of a terminal device and to execute the steps of the method for optimizing the variable speed limit control of a vehicle.
Example 4
In one embodiment of the present disclosure, a terminal device is provided, which includes a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions which are suitable for being loaded by a processor and executing the steps of the vehicle variable speed limit control optimization method.
The above-described embodiments 2, 3, 4 specifically perform the steps of the method described in embodiment 1.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. A variable speed limit control optimization method for a vehicle is characterized by comprising the following steps:
determining a path to be implemented by variable speed limit control, selecting a target road section, and acquiring traffic flow data of the target road section;
constructing a real road network model of a target road section, and carrying the real road network model into the road network model according to the position information of the real electromechanical equipment of the road;
calibrating a road network model according to the traffic flow data of the target road section, and simulating the condition that the number of lanes of a main road is reduced due to accidents of each sub-road section under different service levels by using the road network model;
and constructing a variable speed limit dual-target optimization model, transmitting the simulated traffic flow data under each condition to the variable speed limit dual-target optimization model, and solving a corresponding variable speed limit control strategy by using an NSGA-II algorithm.
2. The method for optimizing the variable speed limit control of the vehicle according to claim 1, wherein the variable speed limit dual-target optimization model selects the NSGA-II algorithm in the multi-target genetic algorithm to optimize with the traffic efficiency and the road safety as targets.
3. The method for optimizing the variable speed limit control of the vehicle according to claim 1, wherein the real road network model of the target road section is constructed by VISSIM.
4. The method as claimed in claim 2, wherein the two optimization objectives of traffic efficiency and road safety are that the average travel time is used as the traffic efficiency index, the collision probability is used as the road safety index, and the constraint condition is determined such that the difference between the speed limit values assigned in adjacent control periods is less than or equal to 10km/h on the same control section and the difference between the speed limit values on adjacent control sections is less than or equal to 10km/h on the same control period.
5. The method for controlling and optimizing the variable speed limit of the vehicle according to claim 1, wherein the step of calibrating the road network model according to the traffic flow data of the target road section and the step of simulating the situation that the number of lanes of the main road is reduced due to accidents of each sub-road section under different service levels by using the road network model comprises the following specific steps: the position of the variable information board is used as a speed-limiting control section, sub-road sections are divided among the sections, traffic running conditions are divided according to service levels, and a road network model generated by VISSIM is used for simulating the condition that the number of main road lanes is reduced due to accidents of each sub-road section under different service levels.
6. The method for optimizing the variable speed limit control of the vehicle according to claim 1, wherein the solving process of the variable speed limit dual-target optimization model is as follows:
an interface of a VISSIM network model and an optimization algorithm is established through Python secondary development; and generating possible speed limit strategies by using an NSGA-II algorithm, inputting the possible speed limit strategies into a VISSIM (visual SIM) network model, calculating to obtain an objective function corresponding to each speed limit strategy through the VISSIM network model, acquiring the objective function value by using the NSGA-II algorithm, carrying out evolution iteration, and searching a new variable speed limit strategy until the evolution is completed.
7. The method for optimizing the variable speed limit control of a vehicle according to claim 1, wherein the NSGA-II algorithm comprises the following steps:
s1: initializing the population according to the scale of the population;
s2: after the obtained initial population is subjected to rapid non-dominated sorting, obtaining first generation filial generations according to the basic operation of a genetic algorithm;
s3: updating the evolution times, adopting an elite strategy, performing rapid non-dominated sorting on the population of the merged parent and the offspring, and screening to obtain a new parent on the basis of calculating the crowding degree; until the number of evolutions is reached, the evolution is completed.
8. A variable speed limit control optimization system for a vehicle, comprising:
the data initialization module is used for determining a path to be implemented by variable speed limit control, selecting a target road section and acquiring traffic flow data of the target road section;
the model building module is used for building a real road network model of a target road section and carrying the real road network model into the road network model according to the position information of the real electromechanical equipment of the road;
the strategy solving module is used for calibrating the road network model according to the traffic flow data of the target road section and simulating the condition that the number of main road lanes is reduced due to accidents of each sub-road section under different service levels by using the road network model; and constructing a variable speed-limiting dual-target optimization model, transmitting the simulated traffic flow data under each condition to the variable speed-limiting dual-target optimization model, and solving a corresponding variable speed-limiting control strategy by using an NSGA-II algorithm.
9. A computer-readable storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute a method for optimizing variable speed limit control of a vehicle according to any one of claims 1 to 7.
10. A terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and to perform a method for optimizing variable speed limit control for a vehicle according to any one of claims 1-7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116978233A (en) * | 2023-09-22 | 2023-10-31 | 深圳市城市交通规划设计研究中心股份有限公司 | Active variable speed limiting method for accident-prone region |
| CN117516562A (en) * | 2024-01-08 | 2024-02-06 | 腾讯科技（深圳）有限公司 | Road network processing method and related device |
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2022
- 2022-11-22 CN CN202211466263.2A patent/CN115862322A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116978233A (en) * | 2023-09-22 | 2023-10-31 | 深圳市城市交通规划设计研究中心股份有限公司 | Active variable speed limiting method for accident-prone region |
| CN116978233B (en) * | 2023-09-22 | 2023-12-26 | 深圳市城市交通规划设计研究中心股份有限公司 | Active variable speed limiting method for accident-prone region |
| CN117516562A (en) * | 2024-01-08 | 2024-02-06 | 腾讯科技（深圳）有限公司 | Road network processing method and related device |
| CN117516562B (en) * | 2024-01-08 | 2024-03-22 | 腾讯科技（深圳）有限公司 | Road network processing method and related device |
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