Intelligent road traffic simulation optimization method and system

Abstract

The invention discloses an intelligent road traffic simulation optimization method and system. The method comprises the following steps: building a road network structure model, and carrying out initial configuration on the road network structure model; historical traffic data of a plurality of time periods in the area range are collected, the historical traffic data of any target time period are restored to the road network structure model, and different path division rules and traffic flow distribution rules are formulated for data simulation; comparing the historical traffic data of the next time period of any target time period with the traffic data in the data simulation process, and if a data error meets a preset error standard, formulating an optimization strategy to carry out green wave optimization on each road section; and selecting an optimal optimization strategy from each data analogue simulation result. According to the technical scheme provided by the embodiment of the invention, the traffic condition of each road section in the area can be analyzed, and the road traffic can be optimized.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of intelligent traffic optimization, and in particular to an intelligent road traffic simulation optimization method and system. Background Art

[0002] With the development of technology, the existing traffic control system tends to adjust the display time of red and green lights according to changes in traffic flow, so as to avoid long red lights in sections with dense traffic and long green lights in sections with sparse traffic, which causes waste of traffic resources.

[0003] The current technical solution is to obtain traffic data of each road section, restore the scene, configure real data parameters, and then calculate the vehicle driving path of each area's traffic flow. However, the simulation results are no more than 40% consistent with the actual basic data. The main reason is that the existing solution does not collect complete vehicle driving paths, and there are serious errors with the actual situation, which ultimately leads to the traffic flow of some sections far exceeding the predicted situation, causing serious congestion on some sections.

[0004] Therefore, how to truly restore the actual traffic conditions of each road section and optimize the road network is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The embodiment of the present invention provides an intelligent road traffic simulation optimization method and system, which can accurately restore the traffic conditions of each road section in a region and optimize the road network structure.

[0006] In a first aspect, an embodiment of the present invention provides an intelligent road traffic simulation optimization method, comprising:

[0007] Building a road network structure model according to a road traffic structure diagram within the regional range, initializing and configuring the road network structure model, and setting a plurality of parking areas in the road network structure model;

[0008] Collect historical traffic data of multiple time periods within the regional scope, restore the historical traffic data of any target time period to the road network structure model, formulate different path division rules and traffic flow allocation rules for data simulation; divide the vehicle driving paths between parking areas based on the path division rules; and allocate the traffic flow of each parking area to each vehicle driving path based on the traffic flow allocation rules;

[0009] Compare the historical traffic data of the next period of any target period with the simulated data in the data simulation process to determine whether the error between the historical traffic data and the simulated data meets the preset error standard. If the error meets the preset error standard, formulate an optimization strategy to optimize the green wave of each road section under the current path division rule and traffic flow allocation rule;

[0010] From the simulation results of each data, the optimization strategy with the shortest simulation time is selected as the best optimization strategy within the regional scope.

[0011] Furthermore, the building of the road network structure model according to the road traffic structure diagram within the regional range includes: identifying the road structure in the road traffic structure diagram, and dividing the road structure into different sections.

[0012] Furthermore, the vehicle driving path is formed by connecting non-repetitive adjacent road sections.

[0013] Furthermore, before performing data simulation, all vehicle driving paths under the current path division rule are calculated.

[0014] Furthermore, the comparison of the historical traffic data of the next time period according to any target time period with the simulation data in the data simulation process includes: comparing the traffic volume of each road section in the next time period through the historical traffic data with the traffic volume of each road section in the simulation data of the corresponding time period;

[0015] The determining whether the error between the historical traffic data and the simulation data meets the preset error standard includes: determining whether there is a certain section of the simulated data vehicle flow whose consistency with the historical traffic data vehicle flow is lower than a first preset error threshold, or determining whether the simulated data vehicle flow of all sections of the road has a consistency with the historical traffic data vehicle flow lower than a second preset error threshold; when there is a certain section of the simulated data vehicle flow whose consistency with the historical traffic data vehicle flow is lower than the first preset error threshold, or when the simulated data vehicle flow of all sections of the road has a consistency with the historical traffic data vehicle flow lower than the second preset error threshold, the error does not meet the preset error standard.

[0016] Furthermore, the optimization strategy adopts simulation optimization of a single path, or simulation optimization of the entire area;

[0017] For the simulation optimization of a single path, simulate any vehicle driving path, set the vehicle driving speed and traffic signal timing in any vehicle driving path, and perform green wave optimization;

[0018] For simulation optimization of the entire area, all vehicle driving paths are simulated, the vehicle driving speeds and traffic signal timings in all vehicle driving paths are set, and green wave optimization is performed.

[0019] Furthermore, the green wave optimization process is: determining the best green light time sequence of the traffic lights at each intersection to form a green wave band;

[0020] The traffic lights at each intersection are coordinated and controlled through a preset algorithm, and the opening hours of the traffic lights at each intersection are coordinated.

[0021] Furthermore, historical traffic data of multiple time periods within the region are collected based on different seasons, weather, temperature, weekdays, holidays, and time periods.

[0022] In a second aspect, an embodiment of the present invention further provides an intelligent road traffic simulation optimization system, the system comprising:

[0023] Model building module, simulation module, data comparison module, strategy selection module;

[0024] The model building module is used to build a road network structure model according to the road traffic structure diagram within the regional range, initialize the configuration of the road network structure model, and set a plurality of parking areas in the road network structure model;

[0025] The simulation module is used to collect historical traffic data of multiple time periods within the regional scope, restore the historical traffic data of any target time period to the road network structure model, formulate different path division rules and traffic flow allocation rules for data simulation; divide the vehicle driving paths between parking areas based on the path division rules; and allocate the traffic flow of each parking area to each vehicle driving path based on the traffic flow allocation rules;

[0026] The data comparison module is used to compare the historical traffic data of the next period of any target period with the simulated data in the data simulation process, and determine whether the error between the historical traffic data and the simulated data meets the preset error standard. If the data error meets the preset error standard, an optimization strategy is formulated to optimize the green wave of each road section under the current path division rule and traffic flow allocation rule;

[0027] The strategy selection module is used to select the optimization strategy with the shortest simulation time from the simulation results of each data as the best optimization strategy within the regional range.

[0028] Furthermore, the model building module is also used to identify the road structure in the road traffic structure diagram and divide the road structure into different sections.

[0029] The embodiment of the present invention performs data simulation by formulating different path division rules and traffic flow distribution rules, and compares historical traffic data with simulation data in the data simulation process to restore a simulation environment close to real road traffic, and performs green wave optimization by formulating optimization strategies, thereby optimizing the road network structure while accurately restoring the traffic conditions of each road section in the area. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 is a flow chart of an intelligent road traffic simulation optimization method provided according to the first embodiment of the present invention;

[0032] Figure 2 is a schematic diagram of a regional road network structure provided according to Embodiment 1 of the present invention;

[0033] Figure 3 It is a structural diagram of an intelligent road traffic simulation optimization system provided according to the second embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0035] Embodiment 1

[0036] Figure 1 This is a flowchart of an intelligent road traffic simulation optimization method provided by an embodiment of the present invention. This embodiment can be applied to the situation of optimizing road traffic on various road sections in a region. The method can be executed by the intelligent road traffic simulation optimization system in the embodiment of the present invention. The system can be implemented in software and / or hardware and can be integrated in a computer device. The computer device can be a mobile terminal or a server. Figure 1 As shown, the method specifically comprises the following steps:

[0037] S101. Building a road network structure model according to a road traffic structure diagram within a regional range, initializing and configuring the road network structure model, and setting a plurality of parking areas in the road network structure model.

[0038] Furthermore, a road network structure model is constructed according to the road traffic structure diagram within the regional range, including: identifying the road structure in the road traffic structure diagram, and dividing the road structure into different sections.

[0039] In an embodiment of the present invention, the recognition of road structure can adopt various image recognition technologies provided in the system to identify the road contour lines or colors in the road traffic structure diagram and restore the road lines within the regional range. The system can also restore the number of lanes, lane markings, number of intersection traffic lights, etc. of each road section in the road network structure model.

[0040] Afterwards, the system automatically divides the road lines within the area into sections. Any section connects two adjacent forks. The purpose of this division is to identify different vehicle driving paths through the combination of sections. Specifically, the system's built-in editing function can be used to draw road sections, add intersections, connect intersection logic lines, regional signal timing, generate green wave belt diagrams, and other operations to restore the road network structure.

[0041] Preferably, the road network structure model constructed by the system in the embodiment of the present invention can use advanced computer graphics technology and data visualization methods to display traffic simulation results from different angles and scales, including two-dimensional plane diagrams, three-dimensional stereograms, and vehicle movement states from microscopic and macroscopic perspectives. It provides more intuitive and comprehensive traffic information for traffic planners, managers and the public, helps to enhance the understanding and support of all parties, and promotes the smooth implementation of traffic projects.

[0042] In the embodiment of the present invention, the road network structure model is initialized and configured, including: configuring each intersection and each road section according to the actual situation on site. Among them, the configuration of each intersection includes: traffic signal timing at the intersection, etc., configuring the signal light parameters at each intersection according to the pre-prepared basic data, and the phase sequence of the street lights at the intersection. The configuration of each road section includes: the speed limit of the road section vehicle, the initial speed of the road section vehicle, the average speed of the road section vehicle, etc.

[0043] Figure 2 is a schematic diagram of a regional road network structure provided according to an embodiment of the present invention, such as Figure 2 As shown, the road sections are such as S1, S2, S3, etc., and the parking areas are such as A, B, C, etc. In the implementation of this embodiment, a parking area for vehicles entering and a parking area for vehicles exiting are added at the boundary of the area range. In the subsequent vehicle simulation process, all vehicles entering the area simulation enter through the parking area, and then are recovered by returning to the parking area on a certain road section.

[0044] S102, collecting historical traffic data of multiple time periods within the regional scope, restoring the historical traffic data of any target time period into the road network structure model, formulating different path division rules and vehicle flow allocation rules for data simulation.

[0045] In the embodiment of the present invention, the system can collect detailed road network data and traffic flow information through real-time monitoring equipment, and use professional GIS tools for data modeling to build a highly realistic virtual traffic environment. This not only helps to improve the accuracy of simulation, but also provides a reliable basis for traffic planning and management. It ensures the accuracy and reliability of the traffic simulation model and reduces decision-making errors caused by insufficient or incorrect data.

[0046] Real-time monitoring equipment can restore historical traffic data within the region as much as possible. These historical traffic data may include: traffic volume, vehicle driving direction, queue length, vehicle driving speed, traffic density, etc. Real-time monitoring equipment can be: cameras, radars, sensors, etc. The system can accurately identify current traffic conditions, predict future traffic trends and optimize them.

[0047] Furthermore, the vehicle driving paths between the parking areas are divided based on the path division rule; and the traffic flow of each parking area is distributed to each vehicle driving path based on the traffic flow distribution rule.

[0048] Among them, the path division rule can adopt a series of methods and technologies for finding the optimal path or feasible path from the starting point to the end point in a given area, such as Dijkstra algorithm, A* algorithm, grid method, dynamic programming method, intelligent algorithm (genetic algorithm, ant colony algorithm, particle swarm optimization algorithm), etc. These algorithms have been maturely applied and will not be described in detail here.

[0049] Similarly, the traffic flow distribution rules can adopt all-or-nothing distribution method, STOCH distribution method, incremental distribution method, multi-objective optimization algorithm, dynamic traffic distribution algorithm and other methods and techniques to reasonably distribute the traffic to each road in the road network according to certain rules based on the known road network, and then calculate the traffic flow of each section in the road network.

[0050] In the embodiment of the present invention, the vehicle driving path is formed by connecting non-repeating adjacent road sections. Figure 2 As shown in the figure, the vehicle driving path from parking area A to parking area D can be: S1-S3-S6-S7, or S1-S5-S10-S8-S7. If there is no traffic flow in S1-S5-S10-S8-S7 for vehicles from parking area A to parking area D, then the vehicle driving path S1-S5-S10-S8-S7 does not exist in the path division rule. The path division rule is used to allocate the driving routes of vehicles in the parking area, and the traffic flow allocation rule is used to allocate the traffic flow on each driving route in the parking area.

[0051] Furthermore, before performing data simulation, all vehicle driving paths under the current path division rule are calculated.

[0052] The system uses advanced path planning algorithms to calculate the optimal paths between parking areas and traffic nodes, and calculates all paths, which is of great significance for simulating actual traffic flow. The optimized path can significantly reduce vehicle travel time and fuel consumption, and improve overall traffic efficiency. By optimizing path selection, traffic congestion can be effectively alleviated and road capacity can be improved.

[0053] In the embodiment of the present invention, the information of each intersection is selected by the distribution of the traffic volume of each section and the flow volume of each section, and the information such as the section node, direction node, section length, section residual value, and flow node in the intersection information is split into data, and finally the flow data within the time range is split into time periods one by one by the time range of the total amount of data given. Preferably, 3-5 minutes is a time period. After all the inputs are completed, the system automatically generates all the simulated paths from each parking area to different parking areas, as well as the flow on the path.

[0054] Although the initial traffic flow of each parking area is fixed, since each parking area has both outflow and inflow of traffic, there are countless situations in the path division rules and traffic flow allocation rules. The system can automatically generate all the path division rules and traffic flow allocation rules, and combine them with the actual historical traffic data of the next time period to delete the path division rules and traffic flow allocation rules that do not conform to the actual situation.

[0055] Among them, the traffic flow allocation rule can allocate traffic flow according to the traffic flow size of a certain road section. First, determine all vehicle driving paths passing through the road section and allocate traffic flow to each vehicle driving path; then, determine the parking area corresponding to each vehicle driving path; then, adjust the traffic flow allocated to each vehicle driving path according to the traffic flow of each parking area. Finally, the traffic flow of the starting parking area all flows to the target parking area, and the traffic flow of each road section is consistent with the actual situation. At the same time, each vehicle driving path has traffic flow. Based on such rule settings, a variety of different configurations can be obtained, that is, the situation of the traffic flow allocated on each vehicle driving path is not unique.

[0056] S103: Compare the historical traffic data of the next time period of any target time period with the simulation data in the data simulation process.

[0057] Preferably, the next period of any target period is used as the current target period, and the historical traffic data of the next period of the current target period is compared with the simulation data in the data simulation process. This process is repeated to obtain simulation data that is closer to the historical traffic data, and the corresponding path division rules and traffic flow allocation rules are confirmed.

[0058] Specifically, the traffic volume of each road section in the next time period based on historical traffic data is compared with the traffic volume of each road section in the simulation data of the corresponding time period.

[0059] Determining whether the error between the historical traffic data and the simulation data meets the preset error standard includes: determining whether there is a road section where the degree of consistency between the simulated data vehicle flow and the historical traffic data vehicle flow is lower than a first preset error threshold, or determining whether the degree of consistency between the simulated data vehicle flow and the historical traffic data vehicle flow of all road sections is lower than a second preset error threshold. When there is a road section where the degree of consistency between the simulated data vehicle flow and the historical traffic data vehicle flow is lower than the first preset error threshold, or when the degree of consistency between the simulated data vehicle flow and the historical traffic data vehicle flow of all road sections is lower than the second preset error threshold, the error does not meet the preset error standard.

[0060] For example, if the simulated data traffic volume of a certain road section matches the historical traffic data traffic volume less than 85%, it is considered that the simulated data has serious errors. Or: if the simulated data traffic volume of all road sections matches the historical traffic data traffic volume less than 85%, it is considered that the simulated data has serious errors.

[0061] Since vehicles with different driving routes will pass through the same road section, for example: the vehicle driving path from parking area A to parking area C is S1-S3-S4, and the vehicle driving path from parking area B to parking area C is S2-S3-S4, it is necessary to delete the path division rules and traffic flow allocation rules that have huge errors compared with the actual situation based on the traffic volume of each section of the road.

[0062] Furthermore, if the error meets the preset error standard, an optimization strategy is formulated to perform green wave optimization on each road section under the current path division rule and traffic flow allocation rule; if the error does not meet the preset error standard, the current path division rule and traffic flow allocation rule are deleted.

[0063] In an embodiment of the present invention, all vehicle driving paths constitute a vehicle path table, which is imported into simulation parameters. Simulation data is generated based on the vehicle driving paths. After the data simulation is completed, the volume of traffic on the road section during the simulation is calculated based on the number of vehicles passing through the road section, and compared with the traffic volume in historical traffic data. If the degree of agreement reaches more than 85%, the error meets the preset error standard.

[0064] Specifically, import all the vehicle driving routes and traffic flows of the road sections calculated at the beginning, start generating simulation data, and after the data simulation is completed, each vehicle will generate a flow when passing through the road section. The total traffic flow of all sections can be calculated during the simulation, and basic data can be obtained. The traffic flow on all sections obtained can be compared with the actual data. If the degree of consistency reaches more than 85%, it means that the basic data is effective and can continue to be optimized.

[0065] Green wave optimization is performed on each road section by formulating an optimization strategy. Optionally, the optimization strategy can adopt simulation optimization of a single path or simulation optimization of the entire area. For simulation optimization of a single path, simulate any vehicle driving path, set the vehicle driving speed and traffic signal timing in any vehicle driving path, and perform green wave optimization; for simulation optimization of the entire area, simulate all vehicle driving paths, set the vehicle driving speed and traffic signal timing in all vehicle driving paths, and perform green wave optimization.

[0066] For the simulation optimization of a single path, you can simulate the effect of vehicle departure on a single path and simulate the driving of vehicles in the system. A single path refers to one of the vehicle driving paths from one parking area to another. For example, depending on whether the vehicle's driving path is congested, whether it is green wave coordinated, and whether the total vehicle speed has increased, you can choose to adjust the vehicle's driving speed during green wave optimization, such as changing it to 30km / h, or 50km / h, and then simulate a single path to determine whether the data effect has been significantly improved. Through the optimization simulation of a single path, the optimization process is relatively fast.

[0067] For the simulation optimization of the entire area, the basic data obtained from the simulation is used to adjust the display of vehicles at traffic lights at intersections. For example, by adjusting the speed of the vehicle, and then optimizing the green wave, the simulation data is continuously generated and compared, and finally the average speed of vehicles and the number of vehicles in the optimal road section are obtained, so as to adjust the traffic light signal timing and road section speed limit in the entire area, so as to optimize the traffic. Through the optimization simulation of the entire area, the optimization data is more accurate and more in line with reality.

[0068] The green wave optimization process monitors and analyzes the traffic flow and road conditions of the target road in real time, including traffic volume, speed, number of lanes, etc., to determine the optimal green light time sequence for each intersection to form a green wave belt. A certain preset optimization algorithm (such as the maximum and minimum method, time difference control method, etc.) is used to coordinate the opening time of the signal lights at each intersection so that they cooperate with each other to form a green wave effect. It ensures that after passing the first green light, the vehicle can continuously pass through the next multiple green light intersections.

[0069] Optionally, the total number of green wave bands to be optimized is generated in both horizontal and vertical directions, and the green light display order and the time distribution of each green light phase are adjusted for each green wave band. Green wave optimization sets the average vehicle speed and adjusts the signal light time configuration of each intersection to ensure that vehicles can just pass through the intersection within a specific speed.

[0070] Green wave optimization mainly configures the signal light timing at the intersection, selects an initial vehicle speed, such as setting the initial vehicle speed to 40km / h, and generates a basic filter optimization graph. The slope of the graph is consistent, and it must be consistent from the previous quadrant of the intersection to the next quadrant of the intersection. This ensures that when the vehicle passes through the intersection at the current speed, it will arrive at the next intersection with a green light. After all optimizations are completed, the signal timing of all intersections is adjusted.

[0071] Specifically, the system realizes dynamic adjustment of traffic signals through intelligent management of traffic light configuration, such as automatically extending or shortening the traffic light time according to real-time traffic flow to form a green wave belt to reduce vehicle waiting time. It improves the overall smoothness of urban traffic, reduces emission pollution caused by waiting for red lights, and improves residents' travel experience.

[0072] In the embodiment of the present invention, the system provides a set of flexible parameter adjustment mechanisms, allowing users to continuously optimize various parameters in the simulation model according to actual conditions, such as speed limits, signal light timing, etc., to achieve the best simulation effect. Traffic simulation becomes a continuous improvement process, and strategies can be continuously adjusted as urban traffic conditions change to maintain the effectiveness of traffic management.

[0073] Preferably, the system can also combine real-time traffic data and historical traffic data to dynamically analyze the traffic flow of each section in the road network, predict future traffic conditions, and adjust traffic management measures in a timely manner. It enhances the ability of traffic management departments to respond to emergencies, such as traffic accidents, bad weather, etc., and can respond quickly and take effective traffic control measures.

[0074] Preferably, historical traffic data of multiple time periods within a region may be collected for simulation based on different seasons, weather, temperature, weekdays, holidays, time periods, and the like.

[0075] S104. From the simulation results of each data, the time configuration of the traffic signals at each intersection with the shortest simulation time is selected as the optimal traffic signal timing strategy within the regional range.

[0076] In the embodiment of the present invention, the data simulation process is to complete the flow of traffic from the starting parking area to the target parking area. Therefore, the simulation process with the shortest time is considered to be the optimal solution for the passage of vehicles on each road section within the regional scope, and then the corresponding optimization strategy is implemented on each road section and intersection within the regional scope, and the speed limit of the road section and the traffic light timing at the intersection are set.

[0077] The technical solution of this embodiment is to establish a road network structure model, set different path division rules and traffic flow allocation rules in the road network structure model to perform data simulation, compare the simulated data with the actual historical traffic data, screen out the road traffic conditions that are most consistent with the real traffic, and formulate an optimization strategy to optimize the green wave of each road section. Through the technical solution of the present invention, the simulated data is closer to the real situation and more stable. At the same time, by continuously adjusting the simulated running speed of the road section and the intersection signal timing, the road traffic in the area is optimized, the vehicle speed in the area is increased, and the number of vehicle queues is reduced.

[0078] Embodiment 2

[0079] Figure 3 This is a structural diagram of an intelligent road traffic simulation optimization system provided by an embodiment of the present invention. This embodiment can be applied to the situation of optimizing road traffic on various road sections in a region. The system can be implemented in software and / or hardware. The system can be integrated in any device that provides intelligent road traffic simulation optimization functions, such as Figure 3 As shown, the intelligent road traffic simulation optimization system specifically includes: a model building module 10, a simulation module 20, a data comparison module 30, and a strategy selection module 40.

[0080] The model building module 10 is used to build a road network structure model according to the road traffic structure diagram within the regional range, initialize the configuration of the road network structure model, and set a plurality of parking areas in the road network structure model;

[0081] The simulation module 20 is used to collect historical traffic data of multiple time periods within the regional scope, restore the historical traffic data of any target time period to the road network structure model, formulate different path division rules and traffic flow allocation rules for data simulation; divide the vehicle driving paths between parking areas based on the path division rules; and allocate the traffic flow of each parking area to the driving path of each vehicle based on the traffic flow allocation rules.

[0082] Preferably, historical traffic data of multiple time periods within a region are collected based on different seasons, weather, temperature, weekdays, holidays, time periods, and the like.

[0083] The data comparison module 30 compares the historical traffic data of the next time period of any target time period with the simulated data in the data simulation process to determine whether the error between the historical traffic data and the simulated data meets the preset error standard. If the error meets the preset error standard, an optimization strategy is formulated to optimize the green wave of each road section under the current path division rule and traffic flow allocation rule.

[0084] Among them, the optimization strategy adopts the simulation optimization of a single path, or the simulation optimization of the entire area;

[0085] For the simulation optimization of a single path, simulate any vehicle driving path, set the vehicle driving speed and traffic signal timing in any vehicle driving path, and perform green wave optimization;

[0086] For simulation optimization of the entire area, all vehicle driving paths are simulated, the vehicle driving speeds and traffic signal timings in all vehicle driving paths are set, and green wave optimization is performed.

[0087] The green wave optimization process is as follows: determine the best green light time sequence of traffic lights at each intersection to form a green wave band;

[0088] The traffic lights at each intersection are coordinated and controlled through a preset algorithm, and the opening hours of the traffic lights at each intersection are coordinated.

[0089] The strategy selection module 40 is used to select the optimization strategy with the shortest simulation time from the simulation results of each data as the best optimization strategy within the area.

[0090] Furthermore, the model building module 10 is also used to identify the road structure in the road traffic structure diagram and divide the road structure into different sections.

[0091] Furthermore, the simulation module 20 is also used to calculate all vehicle driving paths under the current path division rule before performing data simulation.

[0092] The vehicle driving path is connected by non-repetitive adjacent road sections.

[0093] Furthermore, the data comparison module 30 is also used to compare the traffic volume of each road section in the next time period using the historical traffic data with the traffic volume of each road section in the corresponding time period simulation data.

[0094] The data comparison module 30 is also used to determine whether the error between the historical traffic data and the simulation data meets the preset error standard, including: determining whether there is a section of the simulated data vehicle flow and the historical traffic data vehicle flow consistency lower than a first preset error threshold, or determining whether the simulated data vehicle flow of all sections of the vehicle flow and the historical traffic data vehicle flow consistency lower than a second preset error threshold; when there is a section of the simulated data vehicle flow and the historical traffic data vehicle flow consistency lower than the first preset error threshold, or when the simulated data vehicle flow of all sections of the vehicle flow and the historical traffic data vehicle flow consistency lower than the second preset error threshold, the error does not meet the preset error standard.

[0095] The above-mentioned product can execute the method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0096] Through the technical solution of the present invention, the simulation data is closer to the actual situation and more stable. At the same time, by continuously adjusting the simulated running speed of the road section and the timing of intersection signals, the road traffic in the area is optimized, the vehicle speed in the area is increased and the number of vehicle queues is reduced.

[0097] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0098] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent road traffic simulation optimization method, characterized in that: include: Building a road network structure model according to a road traffic structure diagram within the regional range, initializing and configuring the road network structure model, and setting a plurality of parking areas in the road network structure model; Collect historical traffic data of multiple time periods within the regional scope, restore the historical traffic data of any target time period to the road network structure model, formulate different path division rules and traffic flow allocation rules for data simulation; divide the vehicle driving paths between parking areas based on the path division rules; and allocate the traffic flow of each parking area to each vehicle driving path based on the traffic flow allocation rules; Compare the historical traffic data of the next period of any target period with the simulated data in the data simulation process to determine whether the error between the historical traffic data and the simulated data meets the preset error standard. If the error meets the preset error standard, formulate an optimization strategy to optimize the green wave of each road section under the current path division rule and traffic flow allocation rule; From the simulation results of each data, the optimization strategy with the shortest simulation time is selected as the best optimization strategy within the regional scope.

2. The method according to claim 1, characterized in that The road network structure model is constructed according to the road traffic structure diagram within the regional range, including: identifying the road structure in the road traffic structure diagram, and dividing the road structure into different sections.

3. The method according to claim 2, characterized in that The vehicle driving path is formed by connecting non-repeating adjacent road sections.

4. The method according to claim 1, characterized in that: Before performing data simulation, all vehicle driving paths under the current path division rules are calculated.

5. The method according to claim 1, characterized in that The comparison of the historical traffic data of the next time period according to any target time period with the simulation data in the data simulation process includes: comparing the traffic volume of each road section in the next time period through the historical traffic data with the traffic volume of each road section in the simulation data of the corresponding time period; The determining whether the error between the historical traffic data and the simulation data meets the preset error standard includes: determining whether there is a certain section of the simulated data vehicle flow whose consistency with the historical traffic data vehicle flow is lower than a first preset error threshold, or determining whether the simulated data vehicle flow of all sections of the road has a consistency with the historical traffic data vehicle flow lower than a second preset error threshold; when there is a certain section of the simulated data vehicle flow whose consistency with the historical traffic data vehicle flow is lower than the first preset error threshold, or when the simulated data vehicle flow of all sections of the road has a consistency with the historical traffic data vehicle flow lower than the second preset error threshold, the error does not meet the preset error standard.

6. The method according to claim 1, characterized in that The optimization strategy adopts simulation optimization of a single path or simulation optimization of the entire area; For the simulation optimization of a single path, simulate any vehicle driving path, set the vehicle driving speed and traffic signal timing in any vehicle driving path, and perform green wave optimization; For simulation optimization of the entire area, all vehicle driving paths are simulated, the vehicle driving speeds and traffic signal timings in all vehicle driving paths are set, and green wave optimization is performed.

7. The method according to claim 6, characterized in that The process of green wave optimization is as follows: Determine the optimal green light time sequence of traffic lights at each intersection to form a green wave band; The traffic lights at each intersection are coordinated and controlled through a preset algorithm, and the opening hours of the traffic lights at each intersection are coordinated.

8. The method according to claim 1, characterized in that Collect historical traffic data for multiple time periods within the region based on different seasons, weather, temperature, weekdays, holidays, and time periods.

9. An intelligent road traffic simulation optimization system, characterized in that: include: Model building module, simulation module, data comparison module, strategy selection module; The model building module is used to build a road network structure model according to the road traffic structure diagram within the regional range, initialize the configuration of the road network structure model, and set a plurality of parking areas in the road network structure model; The simulation module is used to collect historical traffic data of multiple time periods within the regional scope, restore the historical traffic data of any target time period to the road network structure model, formulate different path division rules and traffic flow allocation rules for data simulation; divide the vehicle driving paths between parking areas based on the path division rules; and allocate the traffic flow of each parking area to each vehicle driving path based on the traffic flow allocation rules; The data comparison module is used to compare the historical traffic data of the next period of any target period with the simulated data in the data simulation process, and determine whether the error between the historical traffic data and the simulated data meets the preset error standard. If the error meets the preset error standard, an optimization strategy is formulated to optimize the green wave of each road section under the current path division rule and traffic flow allocation rule; The strategy selection module is used to select the optimization strategy with the shortest simulation time from the simulation results of each data as the best optimization strategy within the regional range.

10. The system according to claim 9, characterized in that The model building module is also used to identify the road structure in the road traffic structure diagram and divide the road structure into different sections.

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