Bottleneck road section passing optimization method based on upstream variable speed limit
By optimizing the speed limit value using a multi-factor objective function that comprehensively considers weather, vehicle type, and traffic flow conditions, the efficiency and safety issues of existing variable speed limit control methods in complex environments are solved, achieving efficient, safe, and environmentally friendly traffic management in bottleneck sections.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-12
- Publication Date
- 2026-04-10
AI Technical Summary
Existing variable speed limit control methods consider only one factor and are difficult to achieve effective bottleneck section traffic optimization in complex traffic environments, resulting in low traffic efficiency, high safety risks and serious environmental pollution.
The bottleneck road section traffic optimization method based on upstream variable speed limit determines the maximum speed limit, initializes the population, simulates and calculates the fitness value, and iteratively optimizes the speed limit by comprehensively considering weather, vehicle type and traffic flow status, constructing a multi-factor objective function to improve traffic efficiency and safety and reduce pollution.
Achieving better variable speed limit control in complex traffic environments can improve road traffic efficiency and safety, reduce vehicle exhaust emissions, alleviate congestion in tunnel bottleneck sections, and reduce environmental pollution.
Smart Images
Figure CN121838486A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a traffic optimization method for bottleneck road sections based on upstream variable speed limits. It is applicable to the transportation sector. Background Technology
[0002] With the advent of the information age, the number of vehicles is gradually increasing, bringing great convenience to everyone's travel. Roads provide the foundation for vehicle movement, and as the link connecting cities, they are key to social progress and economic development. The completeness of the road network directly reflects the city's economic status and residents' quality of life, promoting the rapid allocation of resources and bringing convenience to residents' lives.
[0003] In modern transportation systems, tunnels play a crucial role, effectively overcoming terrain obstacles such as mountains and rivers, connecting areas that are otherwise difficult to link directly, and greatly expanding the transportation network. However, due to factors such as differences in road technology levels, the balance between traffic supply and demand, the impact of diversion zones, and road infrastructure conditions, tunnels can experience bottleneck sections, leading to reduced traffic efficiency, increased traffic safety risks, environmental impacts, and increased economic costs.
[0004] Setting speed limits before bottleneck sections can prevent congestion to some extent and speed up the dispersal of congestion in the bottleneck area. Most road sections use fixed speed limits, which are inflexible and fail to achieve good results in various situations. Variable speed limit control can adjust speed limits based on real-time traffic flow status, temperature, humidity, and other factors, and display the adjustments on corresponding devices to control traffic flow speed.
[0005] Existing methods for variable speed limit control often involve relatively singular factors. For example, cellular transmission models and METANET models focus on improving road traffic efficiency, while car-following models prioritize traffic safety. However, in reality, speed limits are influenced by complex factors, making variable speed limit control methods that consider only a single factor insufficient for practical needs. Summary of the Invention
[0006] The technical problem to be solved by this invention is to provide a bottleneck section traffic optimization method based on upstream variable speed limits, in order to address the above-mentioned problems.
[0007] The technical solution adopted in this invention is: a traffic optimization method for bottleneck road sections based on upstream variable speed limits, comprising: The maximum speed limit is determined based on the traffic safety conditions at the current time. The traffic safety conditions include the weather conditions and the number of various types of vehicles entering the variable speed limit control section upstream of the bottleneck section at the current time. Initialize the population, where each individual includes the speed limit code for the variable speed limit control section, and the speed limit value of each individual is constrained by the maximum speed limit value; Simulations are performed based on the speed limit values of each individual and the number of vehicles entering the variable speed limit control section during the current time period. The simulation results are combined with the variable speed limit control objective function to calculate the fitness value of each individual. The objective function is constructed based on the traffic efficiency of the bottleneck section. The optimal speed limit for the variable speed control section in the next time period is determined by iterative optimization based on the fitness values of each individual.
[0008] Determining the maximum speed limit based on the traffic safety situation at the current time period includes: Determine the maximum speed limit based on the weather type for the current time period; Based on the number of vehicles of each type in the current time period, determine the maximum speed limit value corresponding to each vehicle type; The optimal maximum speed limit is determined based on the maximum speed limit corresponding to the weather and the maximum speed limit corresponding to the vehicle type.
[0009] The initial population, where each individual includes a speed limit code for a variable speed limit control section, includes: An adaptive hybrid coding method is used to generate binary codes corresponding to each speed limit value. The adaptive hybrid coding method swaps codes based on the number of times each speed limit value appears and the code length of the actual binary code of each speed limit value, reducing the code length of speed limit values that appear more often and increasing the code length of speed limit values that appear less often.
[0010] The objective function is constructed based on the traffic efficiency and traffic pollution of the bottleneck road segment.
[0011] The control items for traffic efficiency include average driving speed, total traffic volume, average dwell time percentage, and total travel time.
[0012] The control items for traffic pollution include the average value of hazardous substance measurements in vehicle exhaust, which are determined based on the number of vehicles on the bottleneck section at each time point.
[0013] The iterative optimization based on the fitness values of each individual includes: The roulette wheel algorithm is used to select the individual with the highest fitness and retain it. The retained individuals are then subjected to crossover and mutation operations. The crossover operation exchanges one or more gene points between different individuals. The mutation operation includes changing the coding content, lengthening or shortening it.
[0014] A bottleneck section traffic optimization device based on upstream variable speed limits, comprising: The speed limit constraint module is used to determine the maximum speed limit value based on the traffic safety conditions of the current time period. The traffic safety conditions include the weather conditions and the number of various types of vehicles entering the variable speed limit control section upstream of the bottleneck section during the current time period. The population generation module is used to initialize the population. Each individual in the population includes the speed limit value code of the variable speed limit control section, and the speed limit value of the individual is constrained by the maximum speed limit value. The fitness calculation module is used to perform simulations based on the speed limit values of each individual and the number of vehicles entering the variable speed limit control section during the current time period. The simulation results are combined with the variable speed limit control objective function to calculate the fitness value of each individual. The objective function is constructed based on the traffic efficiency of the bottleneck section. The iterative optimization module is used to perform iterative optimization based on the fitness values of each individual to determine the optimal speed limit value for the variable speed limit control section in the next time period.
[0015] A storage medium storing a computer program executable by a processor, wherein the computer program, when executed, implements the steps of the bottleneck section traffic optimization method based on upstream variable speed limits.
[0016] A bottleneck section traffic optimization device has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the bottleneck section traffic optimization method based on upstream variable speed limit.
[0017] The beneficial effects of this invention are as follows: This invention determines the maximum speed limit based on traffic safety conditions, constructs a variable speed limit control objective function based on traffic efficiency and traffic pollution conditions, and comprehensively considers multiple factors including traffic efficiency, pollution, and safety. It can achieve better variable speed limit control in complex and ever-changing traffic environments, effectively improve road traffic efficiency and safety, alleviate congestion problems in bottleneck sections in tunnel environments, and significantly reduce vehicle exhaust emissions and environmental pollution.
[0018] This invention employs adaptive hybrid coding to generate binary codes corresponding to each rate limit value, reducing the code length of rate limit values that occur frequently and increasing the code length of rate limit values that occur less frequently. This optimizes the coding scheme, thereby accelerating computation, saving computational space, and improving the efficiency and practicality of the algorithm. Attached Figure Description
[0019] Figure 1 The flowchart is for an example. Detailed Implementation
[0020] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.
[0021] In the description of this invention, "multiple" means two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.
[0022] Example 1: As Figure 1 As shown, this embodiment is a traffic optimization method for bottleneck road sections based on upstream variable speed limits, specifically including the following steps:
[0023] S100. Based on the traffic safety conditions at the current time, determine the maximum speed limit value. The traffic safety conditions include the weather conditions and the number of various types of vehicles entering the upstream variable speed limit control section of the bottleneck section at the current time.
[0024] S110. Based on the weather type for the current time period, determine the maximum speed limit corresponding to the weather.
[0025] This embodiment sets different maximum speed limit constraints for different weather conditions. The detected weather conditions include light rain, moderate rain, heavy rain, light snow, moderate snow, heavy snow, and fog. Specific speed limit constraints are shown in Table 1. This indicates the speed limit under normal conditions. Weather classification is based on the standards of the China Meteorological Administration.
[0026] use Indicates serial number Maximum speed limit corresponding to weather conditions, such as , Considering the possibility of various weather conditions on the road, the formula for calculating the maximum speed limit under weather conditions is as follows: , This indicates the maximum speed limit under all conditions affected by weather. The values in parentheses are... And other existing values include the weather conditions at the time. For example, if the weather was light rain and foggy, then... At this point, the optimal speed limit should be less than If the weather conditions listed in Table 1 are not available, then... .
[0027] Table 1 Speed Limit Constraints in Different Weather Conditions
[0028] S120. Based on the number of vehicles of each type in the current time period, determine the maximum speed limit value corresponding to the vehicle type.
[0029] According to the "Vehicle Classification for Toll Roads," this invention classifies vehicles into three categories: passenger vehicles, freight vehicles, and special-purpose vehicles. Passenger vehicles are further divided into four categories based on length and passenger capacity, while freight vehicles and special-purpose vehicles are divided into six categories based on length and maximum permissible gross vehicle weight. Considering the special environment inside tunnels, excessive speeds by large vehicles are dangerous; therefore, when there are many large vehicles in a lane, the speed limit should be appropriately reduced. time Lane The number of passenger vehicles is recorded as , time Lane The number of trucks is denoted as , time Lane The number of special-purpose vehicles is recorded as Then in The total number of scheduled passenger buses, freight buses, and special-purpose vehicles is The calculation formula is: ; use Record time, The formula for calculating the proportion of passenger vehicles to the total number of passenger vehicles, freight vehicles, and special-purpose vehicles is as follows: ; use Record time, The formula for calculating the proportion of freight vehicles to the total number of passenger cars, freight cars, and special-purpose vehicles is as follows: ; use Record time, The formula for calculating the proportion of special-purpose vehicles to the total number of passenger cars, freight cars, and special-purpose vehicles is as follows: ; The above formula provides the proportion of each type of passenger vehicle, freight vehicle, and special-purpose vehicle in the total number of vehicles at each time point. When there are many large vehicles, appropriately reducing the speed limit can reduce the probability of accidents to some extent. express The parameter affecting vehicle type at any given time is calculated using the following formula: ; in, Indicates the first Weighting of passenger vehicles Indicates the first Truck weights Indicates the first The weight of specialized vehicles can be determined based on the actual conditions of the tunnel. The optimal speed limit at any given time should be less than Since sampling typically occurs within a single period, it is necessary to calculate the vehicle type influence parameters over a given time period. The parameters affecting vehicle type are denoted as follows: The calculation formula is as follows: ; S130. Determine the optimal maximum speed limit based on the maximum speed limit corresponding to the weather and the maximum speed limit corresponding to the vehicle type.
[0030] ; in, Time period Speed limit.
[0031] S200. Initialize the population. Each individual in the population includes the speed limit value code of the variable speed limit control section, and the speed limit value of the individual is constrained by the maximum speed limit value in step S100.
[0032] In this embodiment, adaptive hybrid coding is used to generate binary codes corresponding to each speed limit value. The adaptive hybrid coding swaps codes based on the number of times each speed limit value appears and the coding length of the actual binary code of each speed limit value, reducing the coding length of speed limit values that appear more often and increasing the coding length of speed limit values that appear less often.
[0033] Genetic algorithms generally use binary encoding directly, so the total encoding length during the genetic algorithm process is: ; in, This represents the total length of the code during the genetic algorithm computation. Indicates the speed limit value The encoding length, Indicates the speed limit value The number of times it occurs. During the calculation process, A larger value indicates a longer encoding length, which in turn makes the calculation more complex and requires more space.
[0034] The value is automatically obtained based on the actual situation and is difficult to change. This embodiment reduces the code length corresponding to frequently occurring speed limit values, thereby reducing... The value of .
[0035] If the maximum speed limit in a tunnel is set to 80 km / h, the actual speed limit within the tunnel is generally set between 40-80 km / h, thus appearing more frequently in calculations. Speed limits between 1-39 km / h, however, appear less often. The speed limit code for the 32-63 km / h range uses 6 bits, while the code for the 64-80 km / h range uses 7 bits. The binary codes for the speed limits in the 0-16 km / h and 64-80 km / h ranges are swapped in ascending order: 0 km / h and 64 km / h are swapped, 1 km / h and 65 km / h are swapped, and so on. Similarly, the binary codes for the speed limits in the 17-30 km / h and 40-63 km / h ranges are swapped in reverse order: 17 km / h and 63 km / h are swapped, 18 km / h and 62 km / h are swapped, and so on. Other speed limit codes remain unchanged. The above method can reduce the encoding length of speed limit values that occur frequently and increase the encoding length of speed limit values that occur less frequently, thereby reducing the computational complexity of the entire calculation process.
[0036] For example, 16 km / h is represented as 10000 in binary, and 64 km / h is represented as 1000000. After swapping them, 16 km / h is represented as 1000000, and 64 km / h is represented as 10000. Using shorter binary codes to represent speeds that occur more frequently can save on the total code length, speed up calculations, and save computational space.
[0037] S300: Based on the speed limit values of each individual and the number of vehicles entering the variable speed limit control section during the current time period, traffic simulation software is used to perform simulations. The simulation results are combined with the variable speed limit control objective function to calculate the fitness value of each individual.
[0038] In this embodiment, the objective function is constructed based on the traffic efficiency and traffic pollution of the bottleneck section. The control items for traffic efficiency include average driving speed, total traffic flow, average dwell time percentage, and total travel time. The control items for traffic pollution include the average value of the hazardous substance measurement value of vehicle exhaust, which is determined based on the number of vehicles on the bottleneck section at each time.
[0039] exist The average speed of vehicles during the time period was The calculation formula is as follows: ; in, express time The average speed of vehicles in the lane Indicates the number of lanes. The calculation formula is , Indicates in Lane Time number is The speed of the vehicle, Indicates in Lane The number of vehicles present at all times. Vehicle speed can be recorded by roadside detection equipment or the vehicle's own speedometer, and the number of vehicles can be recorded by roadside detection equipment. During travel, the faster the average speed of vehicles, the smoother the road is; therefore, in actual calculations, the faster the average speed of vehicles, the better.
[0040] exist Total traffic flow on roads during the time period The calculation formula is: ; in, express Time of the first Traffic density of each lane express Time of the first The average speed of vehicles in each lane. The calculation formula is , For the serial number The length of each lane may vary depending on the presence of curves in the road, so each lane needs to be calculated separately. A larger value indicates a larger road traffic volume, therefore, in actual calculations... The bigger the better.
[0041] Time period Average vehicle dwell time percentage The calculation formula is as follows: ; in, lane In time period The sum of the time vehicles spend inside the vehicle area. lane In time period This is the sum of the travel and dwell times of all vehicles within the congestion zone. Vehicle dwell time refers to the time a vehicle's speed is zero, i.e., the time a vehicle remains stationary during congestion. Longer dwell times indicate more severe congestion; this parameter should be as small as possible in actual calculations.
[0042] Time period Total travel time for vehicles within the area The calculation formula is as follows: ; In actual calculations, the time period The greater the total travel time of vehicles within the road, the smoother the road is. In actual calculations, this parameter should be as large as possible.
[0043] When a gasoline-powered vehicle is running, it produces exhaust fumes containing harmful substances, primarily carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx). x The five hazardous substances mentioned above include sulfur dioxide (SO2) and particulate matter (PM). This embodiment mainly calculates the concentration of these five hazardous substances using... express The formula for measuring harmful substances in the exhaust of a single vehicle at any given time is as follows: ; in, , , , as well as They represent in All vehicles are emitting CO, HC, and NO at all times. x The average concentrations of SO2 and PM2.5, while , , , as well as These are parameters representing the concentration of hazardous substances, which can be designed according to actual conditions. Utilizing... Record time period The average value of harmful substance measurements in automobile exhaust. In actual calculations... A higher value indicates a higher concentration of harmful substances in vehicle exhaust. Therefore, when solving for the optimal variable speed limit control value, it is necessary to ensure that... The value should be as small as possible.
[0044] Let the sampling time period be The corresponding objective function for variable speed limiting control is: ; in Let be the objective function, and the larger the better. Considering that the proportion of average vehicle dwell time and the hazardous substance measurement value are inversely correlated with the speed limit value in the function terms, to facilitate the solution of the extremum of the objective function, the reciprocals of both are taken as function terms in the objective function. The parameter for the average vehicle speed function term. For the parameters of the total road traffic flow function term, The parameter for the function term representing the percentage of average vehicle dwell time. For the parameter of the function term of total vehicle travel time, This is a parameter for the hazardous substance measurement function; the specific parameter value can be set according to the actual situation of the tunnel.
[0045] S400: Based on the fitness values of each individual, perform iterative optimization to determine the optimal speed limit value for the variable speed control section in the next time period.
[0046] In this embodiment, the roulette wheel algorithm is used to select the individual with the highest fitness and retain it, and then perform crossover and mutation operations on the retained individuals.
[0047] Crossover operation: The crossover probability is set to 0.40, with each gene point having the same probability. Due to the different coding lengths for different rate limits, the length of genes with shorter coding values may be extended during the crossover operation. The crossover operation will exchange one or more gene points from different individuals.
[0048] For example, by performing a two-point crossover operation on the codes 100111 and 111001, the 3rd and 4th bits will be swapped to obtain 101011 and 110101.
[0049] Mutation operation: Set the mutation probability to 0.05. Mutation operations include lengthening, shortening, or changing the code length. For example, with lengthening mutation, 101 mutates into 111101; with shortening mutation, 11011 mutates into 110; with code changing mutation, 11101 mutates into 11111.
[0050] Based on the fitness function, new individuals are used to replace those with low fitness.
[0051] The maximum number of rounds is set to 1000. When the solution converges or the number of rounds reaches 1000, the optimal solution is output.
[0052] Example 2: This example is a bottleneck section traffic optimization device based on upstream variable speed limits, specifically including: The speed limit constraint module is used to determine the maximum speed limit value based on the traffic safety conditions of the current time period. The traffic safety conditions include the weather conditions and the number of various types of vehicles entering the variable speed limit control section upstream of the bottleneck section during the current time period. The population generation module is used to initialize the population. Each individual in the population includes the speed limit value code of the variable speed limit control section, and the speed limit value of the individual is constrained by the maximum speed limit value. The fitness calculation module is used to perform simulations based on the speed limit values of each individual and the number of vehicles entering the variable speed limit control section during the current time period. The simulation results are combined with the variable speed limit control objective function to calculate the fitness value of each individual. The objective function is constructed based on the traffic efficiency of the bottleneck section. The iterative optimization module is used to perform iterative optimization based on the fitness values of each individual to determine the optimal speed limit value for the variable speed limit control section in the next time period.
[0053] Example 3: This example is a storage medium that stores a computer program that can be executed by a processor. When the computer program is executed, it implements the steps of the bottleneck section traffic optimization method based on upstream variable speed limit described in Example 1.
[0054] Example 4: This example is a bottleneck section traffic optimization device, which has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the bottleneck section traffic optimization method based on upstream variable speed limit described in Example 1.
[0055] If the aforementioned functions are implemented as 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 this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0056] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.
[0057] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.
[0058] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0059] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0060] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
[0061] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.
Claims
1. A bottleneck section traffic optimization method based on upstream variable speed limits, characterized in that, include: The maximum speed limit is determined based on the traffic safety conditions at the current time. The traffic safety conditions include the weather conditions and the number of various types of vehicles entering the variable speed limit control section upstream of the bottleneck section at the current time. Initialize the population, where each individual includes the speed limit code for the variable speed limit control section, and the speed limit value of each individual is constrained by the maximum speed limit value; Simulations are performed based on the speed limit values of each individual and the number of vehicles entering the variable speed limit control section during the current time period. The simulation results are combined with the variable speed limit control objective function to calculate the fitness value of each individual. The objective function is constructed based on the traffic efficiency of the bottleneck section. The optimal speed limit for the variable speed control section in the next time period is determined by iterative optimization based on the fitness values of each individual.
2. The bottleneck section traffic optimization method based on upstream variable speed limit as described in claim 1, characterized in that, Determining the maximum speed limit based on the traffic safety situation at the current time period includes: Determine the maximum speed limit based on the weather type for the current time period; Based on the number of vehicles of each type in the current time period, determine the maximum speed limit value corresponding to each vehicle type; The optimal maximum speed limit is determined based on the maximum speed limit corresponding to the weather and the maximum speed limit corresponding to the vehicle type.
3. The bottleneck section traffic optimization method based on upstream variable speed limit as described in claim 1, characterized in that, The initial population, where each individual includes a speed limit code for a variable speed limit control section, includes: An adaptive hybrid coding method is used to generate binary codes corresponding to each speed limit value. The adaptive hybrid coding method swaps codes based on the number of times each speed limit value appears and the code length of the actual binary code of each speed limit value, reducing the code length of speed limit values that appear more often and increasing the code length of speed limit values that appear less often.
4. The bottleneck section traffic optimization method based on upstream variable speed limit as described in claim 1, characterized in that, The objective function is constructed based on the traffic efficiency and traffic pollution of the bottleneck road segment.
5. The bottleneck section traffic optimization method based on upstream variable speed limit according to claim 1 or 4, characterized in that, The control items for traffic efficiency include average driving speed, total traffic volume, average dwell time percentage, and total travel time.
6. The bottleneck section traffic optimization method based on upstream variable speed limit according to claim 4, characterized in that, The control items for traffic pollution include the average value of hazardous substance measurements in vehicle exhaust, which are determined based on the number of vehicles on the bottleneck section at each time point.
7. The bottleneck section traffic optimization method based on upstream variable speed limit according to claim 1, characterized in that, The iterative optimization based on the fitness values of each individual includes: The roulette wheel algorithm is used to select the individual with the highest fitness and retain it. The retained individuals are then subjected to crossover and mutation operations. The crossover operation exchanges one or more gene points between different individuals. The mutation operation includes changing the coding content, increasing or decreasing the length.
8. A bottleneck section traffic optimization device based on upstream variable speed limit, characterized in that, include: The speed limit constraint module is used to determine the maximum speed limit value based on the traffic safety conditions of the current time period. The traffic safety conditions include the weather conditions and the number of various types of vehicles entering the variable speed limit control section upstream of the bottleneck section during the current time period. The population generation module is used to initialize the population. Each individual in the population includes the speed limit value code of the variable speed limit control section, and the speed limit value of the individual is constrained by the maximum speed limit value. The fitness calculation module is used to perform simulations based on the speed limit values of each individual and the number of vehicles entering the variable speed limit control section during the current time period. The simulation results are combined with the variable speed limit control objective function to calculate the fitness value of each individual. The objective function is constructed based on the traffic efficiency of the bottleneck section. The iterative optimization module is used to perform iterative optimization based on the fitness values of each individual to determine the optimal speed limit value for the variable speed limit control section in the next time period.
9. A storage medium storing a computer program executable by a processor, characterized in that, When the computer program is executed, it implements the steps of the bottleneck section traffic optimization method based on upstream variable speed limit as described in any one of claims 1 to 7.
10. A bottleneck road section traffic optimization device, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that, When the computer program is executed, it implements the steps of the bottleneck section traffic optimization method based on upstream variable speed limit as described in any one of claims 1 to 7.