Signal timing scheme automatic generation system based on multiple algorithms

Abstract

The invention discloses a signal timing scheme automatic generation system based on multiple algorithms, and the system comprises an index data statistics module which is used for carrying out the statistics of a plurality of preset index data in obtained traffic big data in any traffic region which is delimited in advance, and obtaining a statistical result of the index data; the index algorithm model management module is used for performing result representation on any index in the statistical result by using a corresponding specific index algorithm model; the optimization model generation module is used for constructing a multi-algorithm signal timing optimization model for a plurality of index algorithm models corresponding to the plurality of indexes; and the problem solving module is used for solving the optimization model and generating a signal timing scheme of the target traffic area according to a solving result. According to the invention, through integration of the multi-algorithm model, automatic generation of the traffic signal timing scheme is realized, the efficiency and effect of traffic management are improved, and powerful technical support is provided for sustainable development of urban traffic.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent traffic control, and in particular relates to a signal timing scheme automatic generation system based on multiple algorithms. Background Art

[0002] With the rapid development of urban traffic, the problem of congestion at intersections has become one of the main problems facing many urban traffic in the world. Traffic signal timing is an important strategy to improve urban road congestion and optimize road traffic efficiency. Technologies that use traffic big data for optimization modeling and use multi-objective optimization measures to solve the problem of signal intersection timing optimization have emerged.

[0003] Most of the existing algorithms for automatically generating various signal timing schemes remain at the stage of weighted combination of multiple optimization objectives into a comprehensive optimization objective, or including other control objectives in the constraints through the constraint transfer method. This algorithm still belongs to the optimization model of a single objective and a single algorithm, and cannot obtain the optimal solution for multiple objectives together.

[0004] Therefore, there is still a lack of a multi-objective and multi-algorithm signal timing scheme automatic generation system. Summary of the invention

[0005] In view of this, the present invention proposes a signal timing scheme automatic generation system based on multiple algorithms. By integrating multiple algorithm models, the automatic generation of traffic signal timing schemes is realized, which not only improves the efficiency and effectiveness of traffic management, but also provides strong technical support for the sustainable development of urban transportation.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a multi-algorithm based signal timing scheme automatic generation system, comprising:

[0008] The indicator data statistics module is used to count the preset multiple indicator data in the acquired traffic big data in any pre-defined traffic area to obtain the statistical results of the indicator data;

[0009] The indicator algorithm model management module is used to represent the results of any indicator in the statistical results using the corresponding specific indicator algorithm model;

[0010] The optimization model generation module is used to construct a multi-algorithm signal timing optimization model for multiple indicator algorithm models corresponding to multiple indicators;

[0011] The problem solving module is used to solve the optimization model and generate a signal timing plan for the target traffic area based on the solution results.

[0012] Preferably, the indicator data statistics module divides the traffic areas in the following manner:

[0013] In advance, the average traffic flow distribution of each location in a large area of ​​the city is calculated through previous traffic big data;

[0014] Divide the average traffic flow contour lines based on the average traffic flow distribution, and select the location points where the average traffic flow is greater than the preset threshold as the congested location;

[0015] Cluster all congested locations based on the distribution status of congested locations within the city to obtain clustering results;

[0016] For any clustering result, the average total traffic flow value of all congested locations in the clustering result is counted, and the corresponding contour value is determined based on a preset average total traffic flow value-contour value table;

[0017] The contour line value is used to divide an area that can include all congested locations in the clustering results as the traffic area.

[0018] Preferably, the process of dividing the traffic area by using the contour line values ​​includes:

[0019] All congested locations within the clustering results are determined, and a clustering area is obtained by connecting multiple congested locations at the outermost edge of the clustering results;

[0020] The clustering area is expanded based on a preset clustering distance threshold to obtain a second type of area;

[0021] For the area outside the second type of area, select the contour lines that meet the contour line values ​​in all directions and are closest to the second type of area, and connect them smoothly to obtain a closed area division curve;

[0022] It is determined that the area covered by the area dividing curve is the traffic area.

[0023] Preferably, based on any indicator in the statistical results, using a corresponding specific indicator algorithm model to represent the result includes:

[0024] Determine the average phase vehicle delay within the traffic area based on statistical results:

[0025]

[0026] Among them, y i represents the average delay of vehicles in the i-th phase, T is the signal period, γ i is the phase green light signal ratio, x i is the saturation expressed as the ratio of the arriving traffic volume to the traffic capacity, q i is the traffic volume on the import road;

[0027] The average delay of vehicles in each phase is weighted to obtain the average delay of vehicles in the cycle.

[0028]

[0029] Count the average number of stops of vehicles at signal light intersections m i :

[0030]

[0031] Among them, k i It represents the ratio of the vehicle arrival rate to the saturation flow rate at the entrance lane;

[0032] The weighted phase average parking times are calculated by the following formula to obtain the cycle average parking times.

[0033]

[0034] Based on the stop line principle, determine the traffic capacity X within the cycle:

[0035]

[0036] Among them, S i is the saturated flow of vehicles under the signal light phase.

[0037] Preferably, for multiple indicator algorithm models corresponding to multiple indicators, building a multi-algorithm signal timing optimization model includes:

[0038] The multi-algorithm and multi-objective optimization model of signal timing is designed by using the average vehicle delay in the cycle, the average number of stops in the cycle, and the traffic capacity within the cycle:

[0039]

[0040] Among them, F i (θ) represents the objective function of n optimization objectives, θ is the adjustment parameter, is the average delay of vehicles in the cycle, is the average number of stops per cycle, is the traffic capacity within the cycle, is the average vehicle delay in the current cycle, is the average number of parking times in the current cycle, is the traffic capacity in the current cycle, i is the signal cycle phase;

[0041] Create the following constraints:

[0042] The signal cycle length T is between the maximum cycle T max and the minimum period T minbetween;

[0043] Effective green light time g i In the shortest green time g i，min and the longest green time g i，max and the cycle length T is equal to the effective green light length of each phase plus the total signal loss time L;

[0044] The vehicle flow saturation is set between 0.70 and 0.90;

[0045] The constraints are collectively expressed as:

[0046]

[0047] Based on the multi-algorithm and multi-objective optimization model and constraint conditions, a multi-algorithm signal timing optimization model is obtained.

[0048] Preferably, solving the optimization model and generating a signal timing scheme for the target traffic area according to the solution result includes:

[0049] The optimization model is solved using a genetic algorithm, where the calculation process of the genetic algorithm is as follows:

[0050] Step S1, initialize the population, use a random function to generate multiple initial individual populations and select the number of populations according to the dimension of the multi-objective optimization problem, set the maximum number of iterations and initialize the current number of iterations:

[0051] Step S2: determine whether the current number of iterations satisfies the condition that it is greater than the maximum number of iterations. If so, terminate the calculation process and output the optimal solution. If not, proceed to the next step.

[0052] Step S3, applying the fast non-dominated sorting strategy to the population and specifying the fitness of each layer;

[0053] Step S4, generate the next generation through the crossover mutation operator and merge it with its parent population;

[0054] Step S5, quantitatively evaluating population diversity to obtain population diversity;

[0055] Step S6: If the diversity is greater than the set value, proceed to the next step; otherwise, proceed to the next step after dynamically updating the population;

[0056] Step S7: In the design of crowding distance, use the elite strategy to select N individuals to become the new parent population;

[0057] Step S8, increase the number of iterations by one, and return to step S2;

[0058] According to the obtained optimal solution, the optimal signal timing plan is obtained.

[0059] Preferably, the crossover mutation operator includes:

[0060] For each vector l in generation t t , randomly select 3 different target vectors Generate mutant individuals through mutation scheme

[0061]

[0062] Where i represents the i-th vector in generation t, and F is the mutation rate expressed as a scaling factor of the difference vector;

[0063] The next generation of experimental population is obtained through binomial distribution crossover

[0064]

[0065] in, represents the j-dimensional component of the i-th vector, d represents the total number of dimensions, and r i is a random number in the closed interval from 0 to 1, C r is the crossover coefficient used to control the crossover rate or probability.

[0066] Preferably, dynamically updating the population includes:

[0067] When the population size drops to a preset critical value, the part of the population whose fitness is higher than the average fitness of the population is retained, and the other parts are updated:

[0068]

[0069] in, represents the population of the j-dimensional component of the ith vector in the t+1th generation after dynamic update, is the population of the j-dimensional component of the ith vector in the tth generation before the update, rand(0,1) represents a random function with a value between 0 and 1, is the lower bound of the j-th dimension vector, is the upper bound of the j-th dimension vector, is the preset j-dimensional vector value, is the average value of the j-th dimension vector of all individuals in the t-th generation.

[0070] The present invention has achieved at least the following beneficial effects:

[0071] 1. The present invention realizes the automatic generation of traffic signal timing schemes by integrating multiple algorithm models, which not only improves the efficiency and effectiveness of traffic management, but also provides strong technical support for the sustainable development of urban transportation.

[0072] 2. Through intelligent traffic area division, it provides an efficient, flexible and environmentally friendly solution for urban traffic management.

[0073] 3. Through the application of multi-index algorithm model, a scientific and accurate analysis tool is provided for urban traffic management, which helps to optimize traffic flow and improve the urban traffic environment.

[0074] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art may be taught from the practice of the present invention. The objectives and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0076] Figure 1 A schematic diagram of the structure of a system for automatically generating a signal timing scheme based on multiple algorithms in an embodiment of the present invention;

[0077] Figure 2 The figure is a flow chart of the steps for solving the optimization model in an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0079] The present invention provides a multi-algorithm based signal timing scheme automatic generation system, referring to Figure 1 ,include:

[0080] The indicator data statistics module is used to count the preset multiple indicator data in the acquired traffic big data in any pre-defined traffic area to obtain the statistical results of the indicator data;

[0081] The indicator algorithm model management module is used to represent the results of any indicator in the statistical results using the corresponding specific indicator algorithm model;

[0082] The optimization model generation module is used to construct a multi-algorithm signal timing optimization model for multiple indicator algorithm models corresponding to multiple indicators;

[0083] The problem solving module is used to solve the optimization model and generate a signal timing plan for the target traffic area based on the solution results.

[0084] The working principle and beneficial effects of the above technical solution are as follows: first, statistical analysis is performed on the big data in any preset traffic area to extract key indicator data. Then, for each indicator data, the system uses specific algorithm models for in-depth analysis, which can accurately describe and predict the dynamic changes of traffic flow. On this basis, the system constructs a signal timing optimization model integrating multiple algorithms, which can comprehensively consider the influence of various indicators and find the optimal signal timing scheme through the synergy between algorithms. Finally, the system solves the optimization model, automatically generates and outputs the signal timing scheme for the target traffic area. The beneficial effects of this technical solution are mainly reflected in the following aspects: First, it greatly improves the automation and intelligence level of traffic signal timing, reduces manual intervention, and reduces labor costs. Secondly, through precise data analysis and multi-algorithm optimization, the system can generate a more reasonable signal timing scheme, effectively reduce traffic congestion, and improve road capacity. In addition, the optimized signal timing scheme can also reduce the delay and parking times of vehicles at intersections, thereby reducing fuel consumption and exhaust emissions, and has significant environmental benefits. At the same time, the system can also improve the safety of traffic flow and reduce traffic accidents caused by unreasonable signal timing. Finally, the flexibility and adaptability of the system enable it to be applied to a variety of traffic environments and conditions, and has a wide range of application prospects. In summary, the technical solution of the present invention realizes the automatic generation of traffic signal timing schemes by integrating multiple algorithm models, which not only improves the efficiency and effectiveness of traffic management, but also provides strong technical support for the sustainable development of urban transportation.

[0085] In a preferred embodiment, the indicator data statistics module divides the traffic area in the following manner:

[0086] In advance, the average traffic flow distribution of each location in a large area of ​​the city is calculated through previous traffic big data;

[0087] Divide the average traffic flow contour lines based on the average traffic flow distribution, and select the location points where the average traffic flow is greater than the preset threshold as the congested location;

[0088] Cluster all congested locations based on the distribution status of congested locations within the city to obtain clustering results;

[0089] For any clustering result, the average total traffic flow value of all congested locations in the clustering result is counted, and the corresponding contour value is determined based on a preset average total traffic flow value-contour value table;

[0090] The contour line value is used to divide an area that can include all congested locations in the clustering results as the traffic area.

[0091] The working principle and beneficial effects of the above technical solution are as follows: using historical traffic big data, statistical analysis is performed on the average traffic flow of each location in a large area of ​​the city level to identify the distribution pattern of traffic flow. Subsequently, the system divides contour lines based on these average traffic flow data and screens out congestion locations where the average traffic flow exceeds the preset threshold. Through the clustering algorithm, the system groups these congestion locations to identify areas with similar traffic characteristics. For each clustering result, the system calculates the total average traffic flow of all congested locations, and determines the corresponding contour value by comparing it with the preset contour value table, and finally uses the contour value to divide a traffic area containing all congested locations. The beneficial effects of this technical solution are reflected in many aspects: first, it improves the accuracy of traffic area division through data analysis and clustering algorithms, ensuring that the signal timing plan is more in line with the actual traffic conditions. Secondly, this method can dynamically adapt to changes in traffic flow and provide a flexible solution for traffic management. The method of dividing areas using contour lines can accurately screen specific flow location areas and achieve smooth segmentation of areas. In addition, by accurately dividing traffic areas, the system can formulate more refined signal timing plans for specific traffic conditions, effectively alleviate traffic congestion, and improve road use efficiency. At the same time, this method also helps to reduce environmental pollution and energy waste caused by traffic congestion, and has important environmental and economic benefits. In short, the technical solution of the present invention provides an efficient, flexible and environmentally friendly solution for urban traffic management through intelligent traffic area division.

[0092] In a preferred embodiment, the process of dividing the traffic area using the contour line values ​​includes:

[0093] All congested locations within the clustering results are determined, and a clustering area is obtained by connecting multiple congested locations at the outermost edge of the clustering results;

[0094] The clustering area is expanded based on a preset clustering distance threshold to obtain a second type of area;

[0095] For the area outside the second type of area, select the contour lines that meet the contour line values ​​in all directions and are closest to the second type of area, and connect them smoothly to obtain a closed area division curve;

[0096] It is determined that the area covered by the area dividing curve is the traffic area.

[0097] The working principle and beneficial effects of the above technical solution are as follows: First, the congestion location is determined by cluster analysis, and the initial cluster area is obtained by connecting the outermost congestion location. Then, the cluster area is expanded based on the preset cluster distance threshold to form the second type of area. This step takes into account the diffusion effect of traffic flow and ensures the comprehensiveness of traffic area division. Next, the system intelligently selects the area outside the second type of area, finds the contour line that meets the contour line value and is closest to the second type of area, and performs smooth connection processing to form a closed area division curve, and finally determines that the area included in the curve is the traffic area. This process not only improves the accuracy and scientificity of traffic area division, but also reduces manual operations and improves efficiency through intelligent means. The beneficial effects of this technical solution are mainly reflected in: improving the level of refinement of traffic management, making the signal timing plan more in line with the actual traffic conditions, effectively alleviating traffic congestion, and improving road traffic efficiency. At the same time, this method can dynamically adapt to changes in traffic flow and has good flexibility and adaptability. By reducing unnecessary waiting and delays, it also helps to reduce vehicle fuel consumption and emissions, and has a positive impact on environmental protection and sustainable development. In addition, accurate traffic area division can also help improve road safety and reduce traffic accidents caused by traffic congestion. In summary, the technical solution of the present invention provides an efficient, flexible and environmentally friendly solution for urban traffic management through intelligent traffic area division, which helps to optimize traffic flow and improve the urban traffic environment.

[0098] In a preferred embodiment, based on any indicator in the statistical results, using a corresponding specific indicator algorithm model to represent the result includes:

[0099] Determine the average phase vehicle delay within the traffic area based on statistical results:

[0100]

[0101] Among them, y i represents the average delay of vehicles in the i-th phase, T is the signal period, γ i is the phase green light signal ratio, x i is the saturation expressed as the ratio of the arriving traffic volume to the traffic capacity, q i is the traffic volume on the import road;

[0102] The average delay of vehicles in each phase is weighted to obtain the average delay of vehicles in the cycle.

[0103]

[0104] Count the average number of stops of vehicles at signal light intersections m i :

[0105]

[0106] Among them, k i It represents the ratio of the vehicle arrival rate to the saturation flow rate at the entrance lane;

[0107] The weighted phase average parking times are calculated by the following formula to obtain the cycle average parking times.

[0108]

[0109] Based on the stop line principle, determine the traffic capacity X within the cycle:

[0110]

[0111] Among them, S i is the saturated flow of vehicles under the signal light phase.

[0112] The working principle and beneficial effects of the above technical solution are as follows: multiple types of indicator algorithm models are provided for multiple indicators, and the statistical results in the traffic area are deeply analyzed by applying specific indicator algorithm models to achieve refined traffic management. First, the system determines the average delay of vehicles in each phase in the traffic area based on the statistical results. This calculation involves key parameters such as signal cycle, phase green light signal ratio, saturation, and import road traffic volume. Then, the system uses the average delay of phase vehicles for weighted calculation to obtain the average delay of period vehicles. This step comprehensively considers the delay of each phase and provides a comprehensive delay indicator for signal timing. In addition, the system also counts the average number of phase stops of vehicles at signal light intersections, and obtains the average number of period stops through weighted calculation, further refining the analysis of traffic flow. Finally, based on the stop line principle, the system determines the capacity within the cycle, providing important reference data for signal timing. The working principle of this technical solution is that by comprehensively considering multiple key traffic parameters and indicators, a specific algorithm model is used to carefully analyze and represent the traffic flow, thereby providing a scientific basis for signal timing. Its beneficial effects are mainly reflected in the following aspects: First, it can more accurately evaluate and predict traffic delays, help traffic management departments optimize signal timing plans, reduce vehicle waiting time, and improve road use efficiency. Secondly, by reducing unnecessary parking and delays, the solution helps to reduce fuel consumption and exhaust emissions, and has a positive impact on environmental protection and sustainable development. In addition, refined traffic flow analysis can also improve road safety and reduce traffic accidents caused by traffic congestion. In summary, the technical solution of the present invention provides a scientific and accurate analysis tool for urban traffic management through the application of a multi-index algorithm model, which helps to optimize traffic flow and improve the urban traffic environment.

[0113] In a preferred embodiment, for multiple indicator algorithm models corresponding to multiple indicators, constructing a multi-algorithm signal timing optimization model includes:

[0114] The multi-algorithm and multi-objective optimization model of signal timing is designed by using the average vehicle delay in the cycle, the average number of stops in the cycle, and the traffic capacity within the cycle:

[0115]

[0116] Among them, F i (θ) represents the objective function of n optimization objectives, θ is the adjustment parameter, is the average delay of vehicles in the cycle, is the average number of stops per cycle, is the traffic capacity within the cycle, is the average vehicle delay in the current cycle, is the average number of parking times in the current cycle, is the traffic capacity in the current cycle, i is the signal cycle phase;

[0117] Create the following constraints:

[0118] The signal cycle length T is between the maximum cycle T max and the minimum period T min between;

[0119] Effective green light time g i In the shortest green time g i，min and the longest green time g i，max and the cycle length T is equal to the effective green light length of each phase plus the total signal loss time L;

[0120] The vehicle flow saturation is set between 0.70 and 0.90;

[0121] The constraints are collectively expressed as:

[0122]

[0123] Based on the multi-algorithm and multi-objective optimization model and constraint conditions, a multi-algorithm signal timing optimization model is obtained.

[0124] The working principle and beneficial effects of the above technical solution are as follows: through a multi-algorithm multi-objective optimization model and strict constraints, a comprehensive optimization of traffic signal timing is achieved. This method not only takes into account improving traffic efficiency, reducing vehicle delays and parking times, but also takes into account the stability and safety of traffic flow. The beneficial effects are mainly manifested in: improving the scientificity and rationality of traffic signal timing, reducing traffic congestion and delays, and improving road traffic capacity; by optimizing the green light time and cycle time, the waiting time and parking times of vehicles at intersections are reduced, and the driving experience is improved; at the same time, reasonable traffic flow saturation control helps to avoid excessive traffic concentration, maintain the stability of traffic flow, and reduce the risk of traffic accidents. In addition, the flexibility and adaptability of the model enable it to adapt to different traffic conditions and urban environments, and has broad application prospects. In summary, the technical solution of the present invention provides an efficient, flexible and environmentally friendly signal timing solution for urban traffic management through refined multi-objective optimization.

[0125] In a preferred embodiment, the optimization model is solved, referring to Figure 2 , and generate the signal timing scheme for the target traffic area based on the solution results, including:

[0126] The optimization model is solved using a genetic algorithm, where the calculation process of the genetic algorithm is as follows:

[0127] Step S1, initialize the population, use a random function to generate multiple initial individual populations and select the number of populations according to the dimension of the multi-objective optimization problem, set the maximum number of iterations and initialize the current number of iterations:

[0128] Step S2: determine whether the current number of iterations satisfies the condition that it is greater than the maximum number of iterations. If so, terminate the calculation process and output the optimal solution. If not, proceed to the next step.

[0129] Step S3, applying the fast non-dominated sorting strategy to the population and specifying the fitness of each layer;

[0130] Step S4, generate the next generation through the crossover mutation operator and merge it with its parent population;

[0131] Step S5, quantitatively evaluating population diversity to obtain population diversity;

[0132] Step S6: If the diversity is greater than the set value, proceed to the next step; otherwise, proceed to the next step after dynamically updating the population;

[0133] Step S7: In the design of crowding distance, use the elite strategy to select N individuals to become the new parent population;

[0134] Step S8, increase the number of iterations by one, and return to step S2;

[0135] According to the obtained optimal solution, the optimal signal timing plan is obtained.

[0136] The working principle and beneficial effects of the above technical solution are as follows: by using genetic algorithms to solve the multi-algorithm multi-objective signal timing optimization model, a signal timing scheme for the target traffic area is generated. The calculation process of the genetic algorithm includes initializing the population, judging the iteration conditions, applying the fast non-dominated sorting strategy, generating a new generation of populations through the crossover mutation operator, evaluating the population diversity, and selecting the elite strategy. This process simulates the natural selection and genetic mechanism, and gradually approaches the optimal solution through iterative search. The working principle is that the genetic algorithm can handle complex multi-objective optimization problems and find the optimal signal timing scheme by simulating the selection, crossover and mutation operations in the natural evolution process. The beneficial effects of this technical solution are mainly reflected in: first, it can efficiently handle multi-objective optimization problems and find a signal timing scheme that balances different objectives (such as reducing delays, the number of stops and improving traffic capacity). Secondly, the iterative nature of the algorithm allows it to gradually improve the solution until the optimal solution is found, which improves the accuracy of the solution. In addition, the randomness and diversity of the genetic algorithm help to avoid local optimality and increase the possibility of finding the global optimal solution. This method also has good adaptability and robustness, and can adapt to different traffic conditions and urban environments. Ultimately, the optimized signal timing scheme helps to improve traffic flow, reduce vehicle delays and stops, improve driving experience, reduce environmental pollution, and improve road safety. This method can effectively improve the level of intelligent urban traffic management, optimize traffic flow, and improve the urban traffic environment.

[0137] In a preferred embodiment, the crossover mutation operator includes:

[0138] For each vector l in generation t t , randomly select 3 different target vectors Generate mutant individuals through mutation scheme

[0139]

[0140] Where i represents the i-th vector in generation t, and F is the mutation rate expressed as a scaling factor of the difference vector;

[0141] The next generation of experimental population is obtained through binomial distribution crossover

[0142]

[0143] in, represents the j-dimensional component of the i-th vector, d represents the total number of dimensions, and ri is a random number in the closed interval from 0 to 1, C r is the crossover coefficient used to control the crossover rate or probability.

[0144] The working principle and beneficial effects of the above technical solution are as follows: through the crossover mutation operator, the genetic algorithm can effectively maintain population diversity, avoid premature convergence, and improve the solution exploration ability and optimization accuracy. This crossover mutation strategy helps the algorithm to balance the trade-offs between different objectives in multi-objective optimization problems and generate a series of high-quality Pareto frontier solutions. In addition, by controlling the crossover rate and mutation rate, the algorithm can adapt to different optimization requirements and problem characteristics, and improve the flexibility and robustness of the optimization process. Ultimately, this leads to a more effective signal timing scheme, which can reduce traffic delays, improve road capacity, optimize traffic flow, reduce energy consumption and environmental pollution, and improve the safety and efficiency of the transportation system. Therefore, the technical solution of the present invention provides an efficient, flexible and adaptable optimization method for urban traffic signal timing through an advanced genetic algorithm crossover mutation operator.

[0145] In a preferred embodiment, dynamically updating the population includes:

[0146] When the population size drops to a preset critical value, the part of the population whose fitness is higher than the average fitness of the population is retained, and the other parts are updated:

[0147]

[0148] in, represents the population of the j-dimensional component of the ith vector in the t+1th generation after dynamic update, is the population of the j-dimensional component of the ith vector in the tth generation before the update, rand(0,1) represents a random function with a value between 0 and 1, is the lower bound of the j-th dimension vector, is the upper bound of the j-th dimension vector, is the preset j-dimensional vector value, is the average value of the j-th dimension vector of all individuals in the t-th generation.

[0149] The working principle and beneficial effects of the above technical solution are as follows: when the population size drops to a preset critical value, the system will retain those individuals whose fitness is higher than the average fitness of the population, and update the other parts. Specifically, for the j-dimensional component of the i-th vector in the t+1th generation after dynamic update, if its fitness is higher than the average level, it will be retained; otherwise, it will be updated within the preset upper and lower bounds through a random function, while considering the pre-set values ​​and the average value of the j-dimensional vector of all individuals in the t-th generation. This dynamic update strategy ensures that the population always contains better solutions, and can introduce new solutions in a timely manner to avoid the algorithm from converging to a local optimum too early. The working principle of the technical solution is that, through the dynamic update mechanism, the genetic algorithm can gradually approach the optimal solution while maintaining the diversity of the population. This method not only helps the algorithm to explore new possible solutions in a complex search space, but also ensures that the population always contains a certain proportion of high-quality solutions, thereby improving the global search ability of the algorithm. The beneficial effects are mainly reflected in: First, the dynamic update strategy improves the adaptability and robustness of the genetic algorithm, enabling it to better cope with various optimization problems. Secondly, by retaining and updating individuals in the population, the algorithm can effectively avoid premature convergence and increase the chance of finding the global optimal solution. In addition, this strategy also helps to balance exploration and utilization, that is, while maintaining population diversity, it can also make in-depth use of existing high-quality solutions. Ultimately, this helps to generate better signal timing plans, improve traffic flow, reduce vehicle delays and parking times, improve driving experience, reduce environmental pollution, and enhance road safety. Therefore, the technical solution of the present invention provides an efficient, flexible and adaptable optimization method for urban traffic signal timing through dynamic updating of the population, which helps to realize the automation and intelligence of traffic management, improve urban traffic conditions, and improve road use efficiency and safety.

[0150] Finally, it should be noted that the above preferred 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 through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A signal timing scheme automatic generation system based on multiple algorithms, characterized in that: include: The indicator data statistics module is used to count the preset multiple indicator data in the acquired traffic big data in any pre-defined traffic area to obtain the statistical results of the indicator data; The indicator algorithm model management module is used to represent the results of any indicator in the statistical results using the corresponding specific indicator algorithm model; The optimization model generation module is used to construct a multi-algorithm signal timing optimization model for multiple indicator algorithm models corresponding to multiple indicators; The problem solving module is used to solve the optimization model and generate a signal timing plan for the target traffic area based on the solution results.

2. A multi-algorithm based signal timing scheme automatic generation system according to claim 1, characterized in that: The indicator data statistics module divides traffic areas in the following ways: In advance, the average traffic flow distribution of each location in a large area of ​​the city is calculated through previous traffic big data; Divide the average traffic flow contour lines based on the average traffic flow distribution, and select the location points where the average traffic flow is greater than the preset threshold as the congested location; Cluster all congested locations based on the distribution status of congested locations within the city to obtain clustering results; For any clustering result, the average total traffic flow value of all congested locations in the clustering result is counted, and the corresponding contour value is determined based on a preset average total traffic flow value-contour value table; The contour line value is used to divide an area that can include all congested locations in the clustering results as the traffic area.

3. A multi-algorithm based signal timing scheme automatic generation system according to claim 2, characterized in that: The process of dividing traffic areas using contour values ​​includes: All congested locations within the clustering results are determined, and a clustering area is obtained by connecting multiple congested locations at the outermost edge of the clustering results; The clustering area is expanded based on a preset clustering distance threshold to obtain a second type of area; For the area outside the second type of area, select the contour lines that meet the contour line values ​​in all directions and are closest to the second type of area, and connect them smoothly to obtain a closed area division curve; It is determined that the area covered by the area dividing curve is the traffic area.

4. A signal timing scheme automatic generation system based on multiple algorithms according to claim 1, characterized in that: Based on any indicator in the statistical results, the corresponding specific indicator algorithm model is used to represent the results, including: Determine the average phase vehicle delay within the traffic area based on statistical results: Among them, y i represents the average delay of vehicles in the i-th phase, T is the signal period, γ i is the phase green light signal ratio, x i is the saturation expressed as the ratio of the arriving traffic volume to the traffic capacity, q i is the traffic volume on the import road; The average delay of vehicles in each phase is weighted to obtain the average delay of vehicles in the cycle. Count the average number of stops of vehicles at signal light intersections m i : Among them, k i It represents the ratio of the vehicle arrival rate to the saturation flow rate at the entrance lane; The weighted phase average parking times are calculated by the following formula to obtain the cycle average parking times. Based on the stop line principle, determine the traffic capacity X within the cycle: Among them, S i is the saturated flow of vehicles under the signal light phase.

5. A multi-algorithm based signal timing scheme automatic generation system according to claim 1, characterized in that: For multiple indicator algorithm models corresponding to multiple indicators, a multi-algorithm signal timing optimization model is constructed, including: The multi-algorithm and multi-objective optimization model of signal timing is designed by using the average delay of vehicles in the cycle, the average number of stops in the cycle, and the traffic capacity within the cycle: Among them, F i (θ) represents the objective function of n optimization objectives, θ is the adjustment parameter, is the average delay of vehicles in the cycle, is the average number of stops per cycle, is the traffic capacity within the cycle, is the average vehicle delay in the current cycle, is the average number of parking times in the current cycle, is the traffic capacity in the current cycle, i is the signal cycle phase; Create the following constraints: The signal cycle length T is between the maximum cycle T max and the minimum period T min between; Effective green light time g i In the shortest green time g i，min and the longest green time g i，max and the cycle length T is equal to the effective green light length of each phase plus the total signal loss time L; The vehicle flow saturation is set between 0.70 and 0.90; The constraints are collectively expressed as: Based on the multi-algorithm and multi-objective optimization model and constraint conditions, a multi-algorithm signal timing optimization model is obtained.

6. A multi-algorithm based signal timing scheme automatic generation system according to claim 1, characterized in that: Solving the optimization model and generating the signal timing plan for the target traffic area based on the solution results include: The optimization model is solved using a genetic algorithm, where the calculation process of the genetic algorithm is as follows: Step S1, initialize the population, use a random function to generate multiple initial individual populations and select the number of populations according to the dimension of the multi-objective optimization problem, set the maximum number of iterations and initialize the current number of iterations: Step S2: determine whether the current number of iterations satisfies the condition that it is greater than the maximum number of iterations. If so, terminate the calculation process and output the optimal solution. If not, proceed to the next step. Step S3, applying the fast non-dominated sorting strategy to the population and specifying the fitness of each layer; Step S4, generate the next generation through the crossover mutation operator and merge it with its parent population; Step S5, quantitatively evaluating population diversity to obtain population diversity; Step S6: If the diversity is greater than the set value, proceed to the next step; otherwise, proceed to the next step after dynamically updating the population; Step S7: In the design of crowding distance, use the elite strategy to select N individuals to become the new parent population; Step S8, increase the number of iterations by one, and return to step S2; According to the obtained optimal solution, the optimal signal timing plan is obtained.

7. A multi-algorithm based signal timing scheme automatic generation system according to claim 6, characterized in that: The crossover mutation operators include: For each vector l in generation t t , randomly select 3 different target vectors Generate mutant individuals through mutation scheme Where i represents the i-th vector in generation t, and F is the mutation rate expressed as a scaling factor of the difference vector; The next generation of experimental population is obtained through binomial distribution crossover in, represents the j-dimensional component of the i-th vector, d represents the total number of dimensions, and r i is a random number in the closed interval from 0 to 1, C r is the crossover coefficient used to control the crossover rate or probability.

8. A multi-algorithm based signal timing scheme automatic generation system according to claim 6, characterized in that: Dynamic updates to the population include: When the population size drops to a preset critical value, the part of the population whose fitness is higher than the average fitness of the population is retained, and the other parts are updated: in, represents the population of the j-dimensional component of the ith vector in the t+1th generation after dynamic update, is the population of the j-dimensional component of the ith vector in the tth generation before the update, rand(0,1) represents a random function with a value between 0 and 1, is the lower bound of the j-th dimension vector, is the upper bound of the j-th dimension vector, is the preset j-dimensional vector value, is the average value of the j-th dimension vector of all individuals in the t-th generation.

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