A highway traffic simulation method and system based on cellular automaton traffic flow model
The adaptive grid division and traffic control strategy formulation in the cellular automaton traffic flow model improve the adaptability and efficiency of traffic simulation, addressing the limitations of existing models in handling complex traffic scenes and strategy verification.
Patent Information
- Application Number
- CN202410231683.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-01
- Publication Date
- 2024-12-17
- Estimated Expiration
- 2044-03-01
AI Technical Summary
Existing cellular automata models struggle to adapt to the dynamic characteristics of complex traffic scenes and lack efficient verification mechanisms for traffic control strategies.
A highway traffic simulation method using an adaptive grid division algorithm and a cellular automaton traffic flow model, which includes data preprocessing, dynamic grid adjustment, and formulation of traffic control strategies for verification.
The method enhances the flexibility and adaptability of traffic simulation, improves traffic efficiency, and optimizes traffic management by providing accurate and responsive traffic control strategies.
Smart Images
Figure CN118607153B_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of traffic simulation model establishment, in particular to a highway traffic simulation method and system based on a cellular automaton traffic flow model. Background Art
[0002] In modern transportation systems, highways are important infrastructures. The simulation and management of their traffic flows play a vital role in improving road utilization efficiency, alleviating traffic congestion, and improving traffic safety. In recent years, with the rapid development of computer technology and mathematical modeling methods, traffic flow simulation technology has become a powerful tool for studying and solving traffic problems. Among many traffic flow models, the Cellular Automata (CA) model is widely used in the study of traffic flow because of its simple rules, easy-to-implement computer simulation, and high computational efficiency. The model simulates the movement of vehicles on the road by defining the state of cells and their conversion rules, thereby reflecting the macroscopic characteristics and microscopic behaviors of traffic flow. However, although the cellular automata model has made certain progress in the field of traffic flow simulation, the existing technology still has some shortcomings. First, most cellular automata models use fixed grid division, which is often difficult to adapt to the dynamic characteristics of traffic flow characteristics in complex traffic scenes, such as intersections, ramps, etc. Second, the existing models are relatively rough in the design and implementation verification of traffic control strategies, lacking targeted control strategies and efficient strategy verification mechanisms. Summary of the invention
[0003] In view of the problems existing in the above-mentioned existing highway traffic simulation method and system based on cellular automaton traffic flow model, the present invention is proposed.
[0004] Therefore, the problem to be solved by the present invention is that the existing technology is difficult to adapt to the dynamic characteristics of complex traffic scenes when processing them, and is relatively rough in the design and implementation verification of traffic control strategies.
[0005] To solve the above technical problems, the present invention provides the following technical solutions: a highway traffic simulation method based on a cellular automaton traffic flow model, which includes collecting highway traffic data for preprocessing; designing a highway traffic simulation model based on the cellular automaton traffic flow model, and dynamically adjusting the traffic simulation model through an adaptive grid division algorithm; formulating traffic control strategies, and simulating and implementing the strategies to verify their effects; a database collects data for storage, and implements safety protection measures.
[0006] As a preferred solution of the highway traffic simulation method based on the cellular automaton traffic flow model described in the present invention, the collection of highway traffic data for preprocessing refers to collecting highway road layout information data, traffic flow data, vehicle information data, driving behavior data, environmental factor data, accident record data and road maintenance record data for cleaning and filtering, and converting them into a unified format, determining the scale of the highway simulation model based on the collected data, dividing the highway into adjacent cells on average, and numbering and recording the cells in sequence.
[0007] As a preferred solution of the highway traffic simulation method based on the cellular automaton traffic flow model of the present invention, wherein: the design of the highway traffic simulation model based on the cellular automaton traffic flow model refers to establishing the highway traffic simulation model using the collected highway traffic data, including:
[0008] Calculate the highway vehicle interaction C: in is the number of highway vehicles, is the shortest distance between vehicle k and the rest of the vehicles, is the maximum speed difference between vehicle k and other vehicles, is a very small positive number; the information extraction function P is defined as: in is the vehicle density, and are the mean and standard deviation of vehicle density, is the average vehicle speed, and are the mean and standard deviation of the average vehicle speed, is the parameter to adjust the influence of vehicle interaction, Z is the normalization constant;
[0009] Defining dynamic judgment thresholds for: in is the dynamic judgment threshold at time t, is the basic threshold, is the number of cells considered, Represents the state of the i-th cell at time t, with a range of , is the adjustment parameter; the influence weighting function W is constructed as: in is the position number index of the adjacent cell relative to the current cell, is the superposition of the states of the i-th cell and the j-th cell at time t, is the attenuation coefficient, To adjust the threshold parameters of state changes;
[0010] Combining the above algorithm contents, the highway traffic simulation model B is established as follows: in is the time period from time t to time t+i, is the weighting function of the ith cell, is the number of cells considered, is the state of the i-th cell at time t+i, To adjust the parameters, is the number of adjacent cells considered.
[0011] As a preferred solution of the highway traffic simulation method based on the cellular automaton traffic flow model of the present invention, the method of dynamically adjusting the traffic simulation model by the adaptive meshing algorithm refers to dynamically adjusting the cellular grid in the traffic simulation model by the adaptive meshing algorithm after the highway traffic simulation model is established:
[0012] in is a function that adjusts the grid according to the prediction results, and The future highway vehicle density and average vehicle speed predicted by the LSTM model are respectively To control the adjustment coefficient of the adjustment range, For the existing cellular grid division, To adjust the step size, and are the adjustment spatial gradients of future highway vehicle density and average vehicle speed, This is the adjusted cellular grid of the highway traffic simulation model.
[0013] As a preferred solution of the highway traffic simulation method based on the cellular automaton traffic flow model of the present invention, the traffic control strategy is formulated after the highway traffic simulation model is established:
[0014] in For highway traffic efficiency, and The starting point of the expressway. is the speed limit of the highway at time t, is the vehicle density at position x on the highway at time t, is the number of lanes allowed for use at time t, is the number of vehicles released from the entrance ramp at time t, To show the effect of vehicle density and lane restrictions on speed, The entrance ramp is controlled at a vehicle density of The impact on traffic efficiency under these conditions is calculated iteratively until To maximize, , as well as Form a highway traffic control strategy.
[0015] As a preferred solution of the highway traffic simulation method based on the cellular automaton traffic flow model of the present invention, wherein: the simulation implementation verification strategy effect refers to the formation of a highway traffic control strategy through the highway traffic simulation model simulation implementation, the traffic simulation model output simulation value after re-substitution calculation to obtain the highway traffic efficiency and Compare and judge the effectiveness of the strategy:
[0016] like = , it means that the implementation effect of the formulated highway traffic control strategy is the same as the expected effect, the highway traffic control strategy output is implemented as the final result, and the collection and monitoring of highway traffic data are maintained during the implementation process, and the highway traffic control strategy is regularly updated;
[0017] like < , then the effect of the highway traffic control strategy formulated by the life span is worse than the expected effect, and the highway traffic data is collected again to formulate and simulate the traffic control strategy. The simulated values output by the traffic simulation model are collected to calculate the highway traffic efficiency and compare it with the initial highway traffic efficiency until it appears = After stopping the iterative calculation, the traffic control strategy is output.
[0018] As a preferred solution of the highway traffic simulation method based on the cellular automaton traffic flow model described in the present invention, the database collects data for storage and implements security protection measures, which means that the database collects highway traffic data, the established highway traffic simulation model, the traffic simulation model cellular grid dynamic adjustment algorithm and dynamic adjustment results, the formulated highway traffic control strategy and the strategy effect evaluation results for storage, and sets security access rights and access passwords, and the database regularly performs integrity checks on the stored data.
[0019] A highway traffic simulation system based on a cellular automaton traffic flow model, characterized in that:
[0020] Data acquisition module, used to collect highway traffic data and perform preprocessing;
[0021] A model building module is used to build a highway traffic simulation model based on the collected highway traffic data and dynamically adjust the cellular grid of the traffic simulation model through an adaptive grid division algorithm;
[0022] Strategy formulation module, used to formulate highway traffic control strategies and verify the effectiveness of strategies through traffic simulation model simulation implementation;
[0023] The data storage module is used to store data and implement security protection measures, and cooperate with other modules to complete data storage and data access.
[0024] A computer device comprises: a memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the highway traffic simulation method based on the cellular automaton traffic flow model are implemented.
[0025] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned highway traffic simulation method based on a cellular automaton traffic flow model.
[0026] The beneficial effects of the present invention are as follows: the present invention establishes a highway traffic simulation model through the collected highway traffic data and dynamically adjusts the traffic simulation model cellular grid through an adaptive grid division algorithm, thereby realizing highway traffic simulation, greatly improving the flexibility and adaptability of the model, and at the same time formulating a traffic control strategy for evaluation and implementation, effectively improving the highway traffic efficiency and optimizing the highway traffic management process. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
[0028] Figure 1 It is a flowchart of the highway traffic simulation method based on the cellular automaton traffic flow model.
[0029] Figure 2 Schematic diagram of the generation process of the highway traffic simulation model.
[0030] Figure 3 Schematic diagram of the process of formulating highway traffic control strategies.
[0031] Figure 4 It is a structural diagram of the highway traffic simulation system based on the cellular automaton traffic flow model. DETAILED DESCRIPTION
[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.
[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.
[0034] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments. Example
[0035] Reference Figure 1~Figure 3 , which is the first embodiment of the present invention, and which provides a highway traffic simulation method based on a cellular automaton traffic flow model. The highway traffic simulation method based on a cellular automaton traffic flow model comprises the following steps:
[0036] S1, collect highway traffic data for preprocessing;
[0037] Specifically, collecting highway traffic data for preprocessing refers to collecting highway road layout information data, traffic flow data, vehicle information data, driving behavior data, environmental factor data, accident record data, and road maintenance record data for cleaning and filtering, and converting them into a unified format, determining the scale of the highway simulation model based on the collected data, dividing the highway into adjacent cells on average, and numbering and recording the cells in sequence.
[0038] By preprocessing highway traffic data, the data base accuracy and reliability of the simulation model can be significantly improved. Cleaning and filtering out outliers and noise data can ensure the authenticity of the model simulation results, thereby providing more reliable decision-making support for traffic management and planning. Converting various types of traffic data into a unified format is the key to achieving efficient data processing and analysis, which not only facilitates data storage and management, but also supports the application of various data analysis tools and algorithms, enhancing data availability and interoperability. Determining the scale of the highway simulation model based on the collected data is the basis for building an accurate model. This step ensures that the model can truly reflect the physical properties and traffic flow characteristics of the highway, and provide accurate spatial and temporal references for the simulation. Numbering and recording the cells makes the cellular grid division of the highway clear and traceable, facilitating the search for adjacent cells and the dynamic adjustment of the cellular grid, which is helpful to establish a highway traffic simulation model.
[0039] S2. Design a highway traffic simulation model based on the cellular automaton traffic flow model, and dynamically adjust the traffic simulation model through an adaptive grid division algorithm;
[0040] Specifically, designing a highway traffic simulation model based on a cellular automaton traffic flow model refers to establishing a highway traffic simulation model using collected highway traffic data, including:
[0041] Calculate the highway vehicle interaction C: in is the number of highway vehicles, is the shortest distance between vehicle k and the rest of the vehicles, is the maximum speed difference between vehicle k and other vehicles, is a very small positive number; the information extraction function P is defined as: in is the vehicle density, and are the mean and standard deviation of vehicle density, is the average vehicle speed, and are the mean and standard deviation of the average vehicle speed, is the parameter to adjust the influence of vehicle interaction, Z is the normalization constant;
[0042] Defining dynamic judgment thresholds for: in is the dynamic judgment threshold at time t, is the basic threshold, is the number of cells considered, Represents the state of the i-th cell at time t, with a range of , is the adjustment parameter; the influence weighting function W is constructed as: in is the position number index of the adjacent cell relative to the current cell, is the superposition of the states of the i-th cell and the j-th cell at time t, is the attenuation coefficient, To adjust the threshold parameter of state change, it is used to adjust the sensitivity of state change; the highway traffic simulation model B is established by integrating the above algorithm content: in is the time period from time t to time t+i, is the weighting function of the ith cell, is the number of cells considered, is the state of the i-th cell at time t+i, To adjust the parameters, is the number of adjacent cells considered, and ≤ .
[0043] By fine-tuning the simulation of the interaction between vehicles and considering environmental factors, the model's ability to accurately simulate the dynamics of highway traffic flow is improved, and it can more realistically reflect the actual traffic conditions. The dynamic judgment threshold and impact weighting function enable the model to adjust parameters according to real-time data, improve the response capability to emergencies (such as traffic accidents, road construction), and enhance the practicality and scope of application of the model. The information extraction function and comprehensive algorithm enable the model to not only simulate the current traffic conditions, but also predict future traffic flow changes, provide a scientific basis for traffic management and planning, help reduce traffic congestion, and improve road use efficiency. The model can be used as a decision support tool for highway traffic management, provide data support and theoretical basis for traffic flow control, accident emergency response, road network planning and design, etc., and provide a powerful tool for transportation science research. It can be used to study traffic flow theory, driving behavior patterns, the impact of traffic policies, etc., and promote scientific progress in the field of transportation engineering.
[0044] Furthermore, dynamically adjusting the traffic simulation model through an adaptive meshing algorithm refers to dynamically adjusting the cellular grids in the traffic simulation model through an adaptive meshing algorithm after establishing the highway traffic simulation model: in is a function that adjusts the grid according to the prediction results, and The future highway vehicle density and average vehicle speed predicted by the LSTM model are respectively To control the adjustment coefficient of the adjustment range, For the existing cellular grid division, To adjust the step size, and are the adjustment spatial gradients of future highway vehicle density and average vehicle speed, This is the adjusted cellular grid of the highway traffic simulation model.
[0045] Through adaptive grid division, the model can more finely simulate the flow characteristics under different traffic conditions, such as high-density congestion or low-density flow, thereby improving the accuracy of the overall simulation. The LSTM model is used to predict future traffic flow changes. The adaptive grid division algorithm can dynamically adjust the size and shape of the cellular grid, so that the model can adapt to complex and changeable traffic conditions. The simulation model can provide more accurate data support for traffic managers and help them formulate more effective traffic control strategies and planning decisions, such as planning lane usage. By accurately adjusting the cellular grid, unnecessary calculations are reduced, thereby reducing the simulation time and computing costs, so that larger-scale simulation analysis can be carried out with limited resources. Combined with the long-term memory capacity of LSTM, the model can not only simulate the current traffic conditions, but also predict traffic flow changes in the future, providing strong support for real-time traffic management.
[0046] S3. Develop traffic control strategies and simulate implementation to verify the effectiveness of the strategies;
[0047] Specifically, formulating a traffic control strategy refers to formulating a traffic control strategy after establishing a highway traffic simulation model: in For highway traffic efficiency, and The starting point of the expressway. is the speed limit of the highway at time t, is the vehicle density at position x on the highway at time t, is the number of lanes allowed for use at time t, is the number of vehicles released from the entrance ramp at time t, To show the effect of vehicle density and lane restrictions on speed, The entrance ramp is controlled at a vehicle density of The impact on traffic efficiency under these conditions is calculated iteratively until To maximize, , as well as Form a highway traffic control strategy.
[0048] By precisely controlling the number of vehicles released from the entrance ramp and implementing dynamic speed limits, the risk of collision on the highway can be effectively reduced, especially in dense traffic or bad weather conditions. In addition, reasonable lane allocation and flow management can help reduce sudden lane changes and emergency braking, thereby improving overall driving safety. By precisely controlling the number of vehicles released from the entrance ramp and adjusting the lane usage strategy, it can ensure that traffic resources (such as lanes) are used most effectively and avoid resource waste. It allows traffic managers to dynamically adjust traffic control measures based on real-time data and predictive models to adapt to changing traffic needs and conditions. By reducing traffic congestion and optimizing vehicle speeds, vehicle emissions can be reduced and negative impacts on the environment can be reduced. It provides traffic managers with a scientific decision support tool to help them formulate data-based traffic control strategies and improve the accuracy and effectiveness of decision-making.
[0049] Furthermore, the simulation implementation verification strategy effect refers to the formation of highway traffic control strategy through the highway traffic simulation model simulation implementation, the traffic simulation model output simulation value after re-substitution calculation to obtain the highway traffic efficiency and Compare and judge the effectiveness of the strategy:
[0050] like = , it means that the implementation effect of the formulated highway traffic control strategy is the same as the expected effect, the highway traffic control strategy output is implemented as the final result, and the collection and monitoring of highway traffic data are maintained during the implementation process, and the highway traffic control strategy is regularly updated;
[0051] like < , then the effect of the highway traffic control strategy formulated by the life span is worse than the expected effect, and the highway traffic data is collected again to formulate and simulate the traffic control strategy. The simulated values output by the traffic simulation model are collected to calculate the highway traffic efficiency and compare it with the initial highway traffic efficiency until it appears = After stopping the iterative calculation, the traffic control strategy is output.
[0052] Traffic control strategies are The maximum value is reached, so the calculation is recalculated. There will be no more than By verifying the strategy through simulation implementation, it can be ensured that the traffic control strategy formulated can effectively improve the traffic efficiency of the highway before actual application, and avoid the implementation of ineffective or counterproductive strategies. By continuously collecting traffic data and simulating implementation, the control strategy can be adjusted according to the latest traffic conditions and simulation results to make it more refined and personalized to adapt to the ever-changing traffic environment. This method provides a scientific and systematic decision support tool for traffic management departments to help them formulate and adjust traffic control measures more effectively and improve the efficiency and effectiveness of traffic management. Through simulation implementation and iterative calculation, the traffic control strategy formulated can better adapt to changes in actual traffic flow and road conditions, enhance the flexibility and adaptability of the strategy, and regularly update the generated traffic control strategy to not only cope with current traffic challenges, but also foresee and prepare for possible problems in the future, providing support for the continuous improvement of the transportation system.
[0053] S4. The database collects data for storage and implements security protection measures.
[0054] Specifically, the database collects data for storage and implements security protection measures, which means that the database collects highway traffic data, the established highway traffic simulation model, the traffic simulation model cellular grid dynamic adjustment algorithm and dynamic adjustment results, the formulated highway traffic control strategy and strategy effect evaluation results for storage, and sets security access rights and access passwords. The database regularly performs integrity checks on the stored data.
[0055] Storing highway traffic data and related analysis results in a centralized database can improve the efficiency and reliability of data management, facilitate data query, analysis and backup, and implement security protection measures, such as setting access permissions and passwords, to effectively protect data from unauthorized access or malicious attacks, and ensure data security and privacy. Regular integrity testing can promptly detect and repair data corruption or inconsistencies, ensure that the data in the database is accurate and reliable, and provide solid data support for traffic management and research. The stored data and analysis results can provide a scientific basis for traffic management departments, help formulate or adjust traffic control strategies, optimize traffic flow, and reduce congestion. Centrally stored highway traffic data and simulation model results provide rich resources for traffic science research and support the development and verification of various traffic models and algorithms. Example
[0056] Reference Figure 4 , which is the second embodiment of the present invention, and which is different from the previous embodiment, provides a highway traffic simulation system based on a cellular automaton traffic flow model, characterized in that: it includes,
[0057] Data acquisition module, used to collect highway traffic data and perform preprocessing;
[0058] A model building module is used to build a highway traffic simulation model based on the collected highway traffic data and dynamically adjust the cellular grid of the traffic simulation model through an adaptive grid division algorithm;
[0059] Strategy formulation module, used to formulate highway traffic control strategies and verify the effectiveness of strategies through traffic simulation model simulation implementation;
[0060] The data storage module is used to store data and implement security protection measures, and cooperate with other modules to complete data storage and data access.
[0061] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.
[0062] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.
[0063] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.
[0064] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0065] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.
Claims
1. A highway traffic simulation method based on a cellular automaton traffic flow model, characterized in that: include, Collect highway traffic data for preprocessing; Design a highway traffic simulation model based on the cellular automaton traffic flow model, and dynamically adjust the traffic simulation model through an adaptive grid division algorithm; Formulate traffic control strategies and simulate implementation to verify the effectiveness of the strategies; The database collects data for storage and implements security measures; The collecting of highway traffic data for preprocessing refers to collecting highway road layout information data, traffic flow data, vehicle information data, driving behavior data, environmental factor data, accident record data, and road maintenance record data for cleaning and filtering, and converting them into a unified format, determining the scale of the highway simulation model based on the collected data, dividing the highway into adjacent cells on average, and numbering and recording the cells in order; The design of the highway traffic simulation model based on the cellular automaton traffic flow model refers to establishing the highway traffic simulation model using the collected highway traffic data, including: Calculate the highway vehicle interaction C: in is the number of highway vehicles, is the shortest distance between vehicle k and the rest of the vehicles, is the maximum speed difference between vehicle k and other vehicles, is a very small positive number; the information extraction function P is defined as: in is the vehicle density, and are the mean and standard deviation of vehicle density, is the average vehicle speed, and are the mean and standard deviation of the average vehicle speed, Adjust the parameters of vehicle interaction, Z is the normalization constant; define the dynamic judgment threshold for: in is the dynamic judgment threshold at time t, is the basic threshold, is the number of cells considered, Represents the state of the i-th cell at time t, and its value range is , To adjust the parameters; The influence weighting function W is constructed as: in is the position number index of the adjacent cell relative to the current cell, is the superposition of the states of the i-th cell and the j-th cell at time t, is the attenuation coefficient, To adjust the threshold parameters of state changes; Combining the above algorithm contents, the highway traffic simulation model B is established as follows: in is the time period from time t to time t+i, is the weighting function of the ith cell, is the number of cells considered, is the state of the i-th cell at time t+i, To adjust the parameters, is the number of adjacent cells considered; The dynamic adjustment of the traffic simulation model by the adaptive meshing algorithm refers to dynamically adjusting the cellular grids in the traffic simulation model by the adaptive meshing algorithm after the highway traffic simulation model is established: in is a function that adjusts the grid according to the prediction results, and The future highway vehicle density and average vehicle speed predicted by the LSTM model are respectively To control the adjustment coefficient of the adjustment range, For the existing cellular grid division, To adjust the step size, and are the adjustment spatial gradients of future highway vehicle density and average vehicle speed, This is the adjusted cellular grid of the highway traffic simulation model.
2. The highway traffic simulation method based on the cellular automaton traffic flow model as claimed in claim 1, characterized in that: The traffic control strategy formulation refers to formulating the traffic control strategy after establishing the highway traffic simulation model: in For highway traffic efficiency, and The starting point of the expressway. is the speed limit of the highway at time t, is the vehicle density at position x on the highway at time t, is the number of lanes allowed for use at time t, is the number of vehicles released from the entrance ramp at time t, To show the effect of vehicle density and lane restrictions on speed, The entrance ramp is controlled at a vehicle density of The impact on traffic efficiency under these conditions is calculated iteratively until To maximize, , as well as Form a highway traffic control strategy.
3. The highway traffic simulation method based on the cellular automaton traffic flow model as claimed in claim 2, characterized in that: The simulation implementation verification strategy effect refers to the formation of highway traffic control strategy through the highway traffic simulation model simulation implementation, the traffic simulation model output simulation value after re-substitution calculation to obtain the highway traffic efficiency and Compare and judge the effectiveness of the strategy: like = , it means that the implementation effect of the formulated highway traffic control strategy is the same as the expected effect, the highway traffic control strategy output is implemented as the final result, and the collection and monitoring of highway traffic data are maintained during the implementation process, and the highway traffic control strategy is regularly updated; like < , then the effect of the highway traffic control strategy formulated by the life span is worse than the expected effect, and the highway traffic data is collected again to formulate and simulate the traffic control strategy. The simulated values output by the traffic simulation model are collected to calculate the highway traffic efficiency and compare it with the initial highway traffic efficiency until it appears = After stopping the iterative calculation, the traffic control strategy is output.
4. The highway traffic simulation method based on the cellular automaton traffic flow model as claimed in claim 3, characterized in that: The database collects data for storage and implements security protection measures, which means that the database collects highway traffic data, the established highway traffic simulation model, the traffic simulation model cellular grid dynamic adjustment algorithm and dynamic adjustment results, the formulated highway traffic control strategy and the strategy effect evaluation results for storage, and sets security access rights and access passwords, and the database regularly performs integrity checks on the stored data.
5. A highway traffic simulation system based on a cellular automaton traffic flow model according to any one of claims 1 to 4, characterized in that: include, Data acquisition module, used to collect highway traffic data and perform preprocessing; A model building module is used to build a highway traffic simulation model based on the collected highway traffic data and dynamically adjust the cellular grid of the traffic simulation model through an adaptive grid division algorithm; Strategy formulation module, used to formulate highway traffic control strategies and verify the effectiveness of strategies through traffic simulation model simulation implementation; The data storage module is used to store data and implement security protection measures, and cooperate with other modules to complete data storage and data access.
6. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 4 when executing the computer program.
7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.