Urban grading strategy optimization method and system

Abstract

The invention discloses a city grade timing strategy optimization method and system, and the method comprises the steps: employing an improved Webster delay formula to calculate the vehicle delay time of each intersection in different directions based on original traffic data; according to the vehicle delay time of each intersection in different directions, the traffic states of each intersection are divided into unblocked, mild congestion, moderate congestion and severe congestion; constructing a timing strategy optimization model taking the minimum total delay time as a target function based on the traffic state; the signal lamp period, the phase duration and the phase difference of each intersection serve as timing parameters, and an optimal timing scheme is obtained by iteratively calculating the solution of the timing strategy optimization model; and issuing the optimal timing scheme to a signal lamp controller of each intersection. The method and the device are used for solving the problem that timing schemes between adjacent intersections are not coordinated, so that the road traffic efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation Internet of Things application services, and in particular to a city-level timing strategy optimization method and system. Background Art

[0002] With the continuous expansion of urban scale and the rapid growth of motor vehicle ownership, urban traffic congestion is becoming increasingly serious. Traditional traffic light timing methods are mainly based on fixed time plans or simple inductive control, which is difficult to adapt to the complex and changeable urban traffic flow conditions. Especially during peak hours in the morning and evening, due to the significant increase in traffic flow and the operation of traffic lights according to non-peak hours, vehicles on the main roads have long queues and need to wait for multiple traffic light cycles to pass through the intersection; and during low traffic periods such as nighttime or holidays, traffic lights switch at regular time intervals, resulting in unnecessary idle cycles, wasting road resources and travelers' time.

[0003] In related technologies, geomagnetic sensors and video cameras can be installed at intersections to collect traffic flow data, analyze traffic flow characteristics, and formulate corresponding signal timing plans. This method can adjust signal timing to a certain extent based on the collected real-time traffic data, thereby improving the flexibility of traffic signal control.

[0004] However, since the above methods only focus on the traffic conditions of a single intersection or local area for independent timing optimization, they lack the ability to globally analyze and coordinate the traffic flow distribution patterns of the entire urban road network, resulting in the timing plans between adjacent intersections may be uncoordinated, causing vehicles to frequently encounter red lights when passing through a series of intersections, reducing road traffic efficiency. Summary of the invention

[0005] The present application provides a city-level timing strategy optimization method and system for solving the problem of incoordination of timing plans between adjacent intersections, thereby improving road traffic efficiency.

[0006] In the first aspect, the present application provides a city-level timing strategy optimization method, which obtains the traffic flow, speed, vehicle type, queue length and lane occupancy of each intersection collected by multi-source traffic data collection equipment in real time to obtain the original traffic data; Based on the original traffic data, the improved Webster delay formula is used to calculate the vehicle delay time in different directions at each intersection; The traffic status of each intersection is divided into smooth, lightly congested, moderately congested and severely congested according to the vehicle delay time in different directions at each intersection; Build a timing strategy optimization model with the minimum total delay time as the objective function based on traffic conditions. Abstract the urban road network into a graph structure, where each intersection in the graph structure is a node and each road segment is an edge. The constraint conditions of the timing strategy optimization model include minimum green time constraint, maximum green time constraint, and intersection saturation constraint; Take the signal cycle, phase duration, and phase difference of each intersection as timing parameters, and obtain the optimal timing plan by iteratively calculating the solution of the timing strategy optimization model; Send the optimal timing plan to the signal controllers at each intersection, so that all signal controllers operate according to the optimal timing plan.

[0007] By adopting the above technical solution, by obtaining multi-source traffic data in real time and calculating the delay time based on the improved Webster delay formula, the actual traffic operation status of each intersection can be accurately reflected. After classifying the traffic status of each intersection, build a timing strategy optimization model with the minimum total delay time as the objective function. This model abstracts the urban road network into a graph structure and considers constraints such as minimum green time, maximum green time, and intersection saturation, enabling the optimization process to comprehensively balance the traffic demands of each intersection. By iteratively optimizing with the signal cycle, phase duration, and phase difference as timing parameters, a timing plan that minimizes the total network delay time under various constraint conditions can be obtained. This timing plan can adapt to the dynamic changes of urban traffic flow, improve the road network traffic efficiency, reduce the delay time of vehicles in the road network, and enhance the traffic operation quality.

[0008] Combined with some embodiments of the first aspect, in some embodiments, based on the original traffic data, use the improved Webster delay formula to calculate the vehicle delay time in different directions at each intersection, specifically including: Preprocess the original traffic data to obtain preprocessed traffic data. The preprocessing includes removing abnormal data outside the preset range; Divide the preprocessed traffic data according to time windows, and calculate the average vehicle arrival rate and average service time at each intersection within each time window; Substitute the average vehicle arrival rate and average service time into the improved Webster delay formula to calculate the vehicle delay time in different directions at each intersection.

[0009] By adopting the above technical solutions, preprocessing the original traffic data can eliminate abnormal data and improve data quality. Using the time window division method to calculate the average vehicle arrival rate and average service time can eliminate the influence of short-term random fluctuations. Substituting these preprocessed parameters into the improved Webster delay formula can obtain more accurate calculation results of vehicle delay time. The calculation results reflect the actual traffic conditions at each intersection in different directions, providing reliable delay time data for subsequent traffic state division and timing optimization, and making the entire timing optimization process based on accurate delay assessment.

[0010] Combined with some embodiments of the first aspect, in some embodiments, according to the vehicle delay time at each intersection in different directions, the traffic states of each intersection are divided into unobstructed, slightly congested, moderately congested, and severely congested, specifically including: Taking the average delay time of each intersection at different time periods obtained by statistics as the benchmark threshold; Using the benchmark threshold to calculate the deviation rate between the real-time delay time of each intersection and the benchmark threshold; Constructing a state evaluation index matrix according to the deviation rate, and using the state evaluation index matrix for preliminary clustering to obtain a clustering result; Dividing the traffic states of each intersection into unobstructed, slightly congested, moderately congested, and severely congested according to the clustering result.

[0011] By adopting the above technical solutions, statistical historical data is used to establish a benchmark threshold, and a state evaluation index matrix is constructed using the deviation rate between the real-time delay time and the benchmark threshold, making the division of traffic states have an objective evaluation standard. Based on the state evaluation index matrix for clustering analysis, intersections with similar traffic characteristics can be classified into corresponding traffic state levels, reducing the error caused by subjective judgment, making the division result of traffic states more objective and accurate, being able to truly reflect the congestion degree of each intersection, and helping to accurately identify problem sections in the road network.

[0012] Combined with some embodiments of the first aspect, in some embodiments, a timing strategy optimization model with the minimum total delay time as the objective function is constructed based on the traffic state, specifically including: Calculating the traffic capacity coefficient of each road section based on the traffic state of each intersection; Establishing a road network connectivity constraint matrix, and the element values of the road network connectivity constraint matrix are determined according to the distance between adjacent intersections, the vehicle arrival rate, and the green wave band speed; Expressing the total delay time as a function of the signal timing parameters of each intersection according to the traffic capacity coefficient and the road network connectivity constraint matrix, and the function includes a random delay term and a uniform delay term. The random delay term is determined by the randomness of vehicle arrival, and the uniform delay term is determined by the signal timing scheme; Establish mathematical expressions for the minimum green time constraint, the maximum green time constraint, and the intersection saturation constraint. Among them, the minimum green time constraint is determined according to the pedestrian crossing demand, the maximum green time constraint is determined according to the vehicle queue length, and the intersection saturation constraint is determined according to the traffic capacity. Take the function as the objective function and the minimum green time constraint, the maximum green time constraint, and the intersection saturation constraint as the constraint conditions to obtain the timing strategy optimization model.

[0013] By adopting the above technical solutions, calculate the traffic capacity coefficients of each road section and establish a road network connectivity constraint matrix to quantitatively express the operating characteristics of each intersection and road section in the road network. On this basis, the total delay time objective function constructed takes into account two components: random delay and uniform delay, reflecting the impact of the signal timing plan on vehicle delay. At the same time, three types of constraint conditions, namely the minimum green time, the maximum green time, and the intersection saturation, are introduced, making the optimization model consider both the pedestrian crossing demand and the vehicle traffic efficiency, and improving the feasibility and rationality of the timing plan.

[0014] Combined with some embodiments of the first aspect, in some embodiments, after the optimal timing plan is sent to the signal controllers at each intersection, the method further includes: Construct a road network cycle fluctuation curve based on the historical timing data of each intersection. The road network cycle fluctuation curve reflects the change law of the signal cycle at each intersection in the road network; Take the local extreme points of the cycle fluctuation curve in the time dimension as the key time nodes; Calculate the cycle difference degree between two adjacent intersections at the key time nodes; When the cycle difference degree is greater than the preset threshold, mark the road section between two adjacent intersections as a potential bottleneck road section; Conduct reverse timing optimization for the potential bottleneck road section. The reverse timing optimization takes the upstream intersection as the benchmark and reversely adjusts the timing parameters of the downstream intersection to make the traffic capacity of the potential bottleneck road section match the traffic flow.

[0015] By adopting the above technical solutions, constructing a road network cycle fluctuation curve can accurately reflect the dynamic change law of the signal cycle at each intersection in the road network. Combining the calculation of the cycle difference degree at the key time nodes, potential traffic bottleneck road sections can be identified in a timely manner. Conduct reverse timing optimization for the identified potential bottleneck road sections, and reversely adjust the timing parameters of the downstream intersection with the upstream intersection as the benchmark to make the traffic capacity of the potential bottleneck road section match the actual traffic flow, which can prevent the generation and spread of traffic congestion and improve the overall traffic efficiency of the road network.

[0016] Combined with some embodiments of the first aspect, in some embodiments, constructing a road network cycle fluctuation curve based on the historical timing data of each intersection specifically includes: The historical timing data of each intersection is segmented by using the sliding time window method; Wavelet transform is used to perform multi-scale decomposition on the data within each time window, and the main frequency components of the periodic change are extracted; Based on the main frequency components, the periodic change trend is reconstructed to generate a smooth road network periodic fluctuation curve.

[0017] By adopting the above technical solution, the historical timing data is segmented by using the sliding time window method, and combined with the multi-scale decomposition technology of wavelet transform, the main frequency components of the periodic change can be effectively extracted, and a smooth road network periodic fluctuation curve is generated through reconstruction. The random fluctuations and noise interferences in the timing data can be filtered out, the real periodic change characteristics are retained, the extraction accuracy of the periodic fluctuation characteristics is improved, the subsequent identification of bottleneck sections based on the curve is more accurate and reliable, and a more accurate decision-making basis for road network timing optimization is provided.

[0018] Combined with some embodiments of the first aspect, in some embodiments, the periodic difference degree between two adjacent intersections is calculated at key time nodes, specifically including: An intersection signal timing feature vector is established, including the cycle length, green ratio, phase difference and flow direction distribution of the intersection signal timing feature vector; The feature vector distance between the timing feature vectors of two adjacent intersections is calculated by using the multi-dimensional feature distance calculation method based on the improved cosine similarity; The road section saturation weight coefficient is calculated according to the road section traffic capacity and actual demand; The feature vector distance is multiplied by the saturation weight coefficient to obtain the periodic difference degree.

[0019] By adopting the above technical solution, multiple key parameters affecting intersection timing coordination are comprehensively considered, and these parameters are uniformly expressed and processed in the form of feature vectors. The improved cosine similarity calculation method reduces the limitations of the traditional Euclidean distance in dealing with high-dimensional features and can more accurately measure the similarity degree of timing schemes. Introducing the saturation weight coefficient takes into account the influence of actual traffic demand on the evaluation of timing differences, making the calculation result of the difference degree more in line with the actual traffic operation characteristics.

[0020] In a second aspect, an embodiment of the present application provides an urban-level timing strategy optimization system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the methods described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions that, when running on a system, cause the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product that, when running on a system, causes the system to execute the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides an urban-level signal timing strategy optimization method. By obtaining multi-source traffic data in real time and calculating the delay time based on the improved Webster delay formula, it can accurately reflect the actual traffic operation status of each intersection. After classifying the traffic status of each intersection, a timing strategy optimization model with the minimum total delay time as the objective function is constructed. This model abstracts the urban road network into a graph structure and considers constraints such as the minimum green time, maximum green time, and intersection saturation, enabling the optimization process to comprehensively balance the traffic demands of each intersection. By iteratively optimizing the signal cycle, phase duration, and phase difference as timing parameters, a timing plan that minimizes the total network delay time under various constraint conditions can be obtained. This timing plan can adapt to the dynamic changes of urban traffic flow, improve the road network traffic efficiency, reduce the delay time of vehicles in the road network, and enhance the traffic operation quality.

[0024] 2. The present application provides an urban-level signal timing strategy optimization method. Calculate the traffic capacity coefficient of each road section and establish a road network connectivity constraint matrix to quantitatively express the operation characteristics of each intersection and road section in the road network. On this basis, the total delay time objective function constructed considers two components: random delay and uniform delay, reflecting the impact of the signal timing plan on vehicle delay. At the same time, three types of constraint conditions, namely the minimum green time, maximum green time, and intersection saturation, are introduced, enabling the optimization model to consider both the pedestrian crossing demand and the vehicle traffic efficiency, improving the feasibility and rationality of the timing plan.

[0025] 3. The present application provides an urban-level signal timing strategy optimization method. Construct a road network cycle fluctuation curve, which can accurately reflect the dynamic change law of the signal cycle at each intersection in the road network. Combining the calculation of the cycle difference degree at key time nodes, potential traffic bottleneck road sections can be identified in a timely manner. Perform reverse timing optimization on the identified potential bottleneck road sections, and adjust the timing parameters of downstream intersections based on the upstream intersections in reverse, so that the traffic capacity of the potential bottleneck road sections matches the actual traffic flow, which can prevent the generation and spread of traffic congestion and improve the overall traffic efficiency of the road network. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flowchart of a method for optimizing urban signal timing strategies in an embodiment of the present application.

[0027] Figure 2 It is another schematic flowchart of a method for optimizing urban signal timing strategies in an embodiment of the present application.

[0028] Figure 3 It is a schematic structural diagram of an entity device of an urban signal timing strategy optimization system provided in an embodiment of the present application. Detailed implementation manners

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" used in the present application refers to any and all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0031] Next, an embodiment is used and combined with Figure 1 , to describe a method for optimizing urban signal timing strategies in an embodiment of the present application: Please refer to Figure 1 , which is a schematic flowchart of a method for optimizing urban signal timing strategies in an embodiment of the present application.

[0032] S101. Real-time obtain the traffic flow, vehicle speed, vehicle type, queue length, and lane occupancy rate of each intersection collected by multi-source traffic data collection devices to obtain the original traffic data; The system needs to obtain the traffic data of each intersection in the city in real time as the input for subsequent optimization. The traffic data can come from a variety of collection devices, such as geomagnetic detectors, video surveillance, microwave vehicle detectors, etc. These devices are distributed at each intersection in the city and can collect data such as the traffic flow at the intersection, the average vehicle speed, the vehicle type composition, the vehicle queue length, and the occupancy rate of each lane in real time. The system will summarize the data from these collection devices to obtain the original traffic data within the entire city.

[0033] In specific implementation, the system can be directly connected to these collection devices and receive the data uploaded by the devices in real time through wired or wireless communication methods. To ensure data timeliness, the system can require the collection devices to report data at a frequency of seconds or minutes. At the same time, the system can also set up data caching to avoid data loss caused by communication interruptions and other reasons. The obtained raw data can be stored in a structured form (such as a relational database) or an unstructured form (such as a text file) to prepare for subsequent calculation and analysis.

[0034] S102. Calculate the vehicle delay time in different directions at each intersection based on the original traffic data using an improved Webster delay formula; The system calculates the vehicle delay time in different directions at each intersection based on the original traffic data using an improved Webster delay formula. Specifically, the original traffic data is preprocessed to obtain preprocessed traffic data. The preprocessing includes removing abnormal data beyond a preset range. Divide the preprocessed traffic data according to time windows and calculate the average vehicle arrival rate and average service time at each intersection within each time window; Substitute the average vehicle arrival rate and average service time into the improved Webster delay formula to calculate the vehicle delay time in different directions at each intersection.

[0035] After obtaining the original traffic data of each intersection, the system needs to evaluate the traffic operation status in different directions at each intersection. The vehicle delay time is one of the commonly used evaluation indicators, which reflects the additional time consumption of vehicles passing through the intersection due to signal lights and interference from other vehicles. The traditional Webster delay formula considers parameters such as vehicle arrival rate, signal cycle, and green light time, but the modeling of the actual road conditions is not fine enough. This step uses an improved Webster delay formula, which can estimate the delay time more accurately.

[0036] Specifically, the system first preprocesses the original traffic data to remove obvious abnormal data points to ensure the reliability of the data. Then, the data is divided according to fixed time windows (such as 5 minutes), and the average vehicle arrival rate and average service time (i.e., the time to pass through the intersection) in different directions at each intersection within each time window are calculated. These two parameters are the key factors affecting the delay. Finally, the average arrival rate and service time are substituted into the improved Webster delay formula to estimate the delay time in different directions at each intersection during each period. Compared with the classical formula, the improved formula may incorporate more parameters reflecting the actual road conditions, such as the number of lanes, turning ratio, and pedestrian crossing time, so as to obtain a more accurate delay estimate value closer to the actual situation.

[0037] S103. Classify the traffic states of each intersection into unobstructed, slightly congested, moderately congested, and severely congested according to the vehicle delay times in different directions of each intersection; The system classifies the traffic states of each intersection into unobstructed, slightly congested, moderately congested, and severely congested according to the vehicle delay times in different directions of each intersection. Specifically: Take the average delay time of each intersection at different time periods obtained by statistics as the benchmark threshold; Calculate the deviation rate between the real-time delay time of each intersection and the benchmark threshold by using the benchmark threshold; Construct a state evaluation index matrix according to the deviation rate, and perform preliminary clustering by using the state evaluation index matrix to obtain a clustering result; Classify the traffic states of each intersection into unobstructed, slightly congested, moderately congested, and severely congested according to the clustering result.

[0038] After calculating the vehicle delay times in each direction of each intersection, the system needs to further judge the traffic states of each intersection to provide a decision-making basis for subsequent signal timing optimization. In this step, the traffic states of intersections are classified into four levels: unobstructed, slightly congested, moderately congested, and severely congested according to the magnitude of the delay time. Among them, unobstructed means that vehicles are basically unobstructed, slightly congested means that vehicles are slightly delayed but the overall traffic is unimpeded, and moderately and severely congested mean that the traffic is seriously blocked and urgently needs to be dredged.

[0039] When specifically implemented, the system first conducts statistical analysis on historical data to obtain the average delay time of each intersection at different time periods, and uses it as the benchmark threshold for dividing the state levels. For example, the delay time lower than 20% of the historical average can be regarded as unobstructed, higher than the average by 20% to 50% as slightly congested, higher than the average by 50% to 100% as moderately congested, and higher than the average by 100% as severely congested. During real-time operation, the system calculates the real-time delay time of each intersection and compares it with the benchmark threshold to obtain the current traffic state level of this intersection.

[0040] Considering the complexity of the actual road conditions, it may be difficult to accurately judge the traffic state based on the delay time at a single moment. For this reason, the system can combine other indicators and adopt a more refined state division method. For example, the change trend of the delay time within a period of time can be calculated. If the delay continues to increase, it indicates that the congestion is intensifying. Another example is to measure the average speed and acceleration of the queuing vehicles. The lower the speed and the more frequent idling, the higher the congestion level. Summarize these indicators into a state evaluation matrix, and then perform clustering analysis on each intersection to obtain a more accurate and comprehensive state discrimination result.

[0041] It should be noted that the state of road congestion evolves dynamically and is affected by factors such as upstream and downstream sections, weather conditions, and accident handling. Therefore, while the system determines the state of the intersection, it also needs to analyze the reasons for the state evolution and estimate its duration and development trend. This helps with decision optimization and resource scheduling. For example, if the system predicts that a certain intersection will change from mild congestion to moderate congestion in the next half an hour, the signal timing of the relevant intersection can be adjusted in advance to guide the traffic to divert in advance and avoid the worsening of congestion. On the contrary, if the congestion state comes from a temporary event, the system can link emergency disposal to shorten the time and space scope of the event.

[0042] S104, constructing a timing strategy optimization model with minimizing total delay time as the objective function based on traffic conditions; Based on the traffic status, the system constructs a timing strategy optimization model with the minimum total delay time as the objective function. The urban road network is abstracted into a graph structure, in which each intersection is a node and each road section is an edge. The constraints of the timing strategy optimization model include the minimum green light time constraint, the maximum green light time constraint and the intersection saturation constraint. Specifically: the capacity coefficient of each road section is calculated based on the traffic status of each intersection; Establish a road network connectivity constraint matrix, the element values ​​of which are determined according to the distance between adjacent intersections, the traffic arrival rate and the green wave speed; According to the capacity coefficient and the road network connectivity constraint matrix, the total delay time is expressed as a function of the signal timing parameters of each intersection. The function includes a random delay term and a uniform delay term. The random delay term is determined by the randomness of vehicle arrival, and the uniform delay term is determined by the signal timing plan. Establish mathematical expressions for the minimum green light time constraint, the maximum green light time constraint and the intersection saturation constraint, where the minimum green light time constraint is determined according to the pedestrian crossing demand, the maximum green light time constraint is determined according to the length of the vehicle queue, and the intersection saturation constraint is determined according to the traffic capacity; The function is used as the objective function, and the minimum green light time constraint, the maximum green light time constraint and the intersection saturation constraint are used as constraint conditions to obtain the timing strategy optimization model.

[0043] After obtaining the traffic status division results of each intersection, the system needs to build a signal timing optimization model based on this and solve the optimal timing solution. The goal of the model is to minimize the total delay time of vehicles and pedestrians in the area while ensuring safety and fairness. This step describes the model construction process in detail.

[0044] First, the system abstracts the entire urban road network into a graph structure model. Among them, intersections correspond to nodes of the graph, and roads correspond to edges connecting nodes. Each node and edge has a series of attributes, such as the node's traffic status level, traffic flow, edge length, speed limit, and traffic capacity. Most of these attributes come from the calculation results of the previous steps, and a small number of parameters (such as road length) come from basic map data. Converting the road network into a graph model is conducive to the implementation of subsequent optimization algorithms.

[0045] Next, the system needs to establish the objective function of the optimization model, that is, the mathematical expression of the total delay time. The total delay is divided into two parts: random delay and uniform delay. Random delay is mainly caused by the randomness of vehicle arrival and is related to the traffic flow at the intersection; uniform delay comes from the stop time of the signal light and is directly related to the timing parameters. When calculating the delay, the system fully considers the actual capacity of the road. For example, the capacity between adjacent intersections depends not only on the length of the section, but also on factors such as the saturated traffic flow of the section, the vehicle arrival rate, and the speed of the green wave belt under coordinated control. The weighted average of the capacity of each section can be used as the capacity coefficient of the entire road network. In addition, the system also defines a road network connectivity matrix, in which each element indicates whether green wave coordination can be achieved between two adjacent intersections. Green wave coordination allows vehicles to pass through multiple intersections continuously at a specific speed without being hindered by traffic lights, but not all sections can meet the green wave conditions such as length and speed. Introducing the capacity coefficient and connectivity matrix into the delay time function can obtain a more realistic delay estimate.

[0046] Finally, the system adds various constraints based on the objective function to build a complete timing optimization model. Common constraints include: Minimum and maximum green light time constraints. To ensure the basic right of way for pedestrians and vehicles in all directions, the green light duration of each phase cannot be less than a certain lower limit; and to avoid long waiting times for other directions due to the green light time, the green light time cannot exceed a certain upper limit. The specific values ​​of the upper and lower limits can be dynamically adjusted according to the pedestrian crossing demand and vehicle flow conditions at each intersection.

[0047] Intersection saturation constraint. Saturation represents the ratio of the number of vehicles passing through an intersection in a cycle to the intersection's traffic capacity. When the saturation exceeds 1, it indicates that the number of vehicles arriving has exceeded the intersection's processing capacity, and vehicles will be backlogged indefinitely, requiring the green light time to be extended. Therefore, the saturation of each intersection should not exceed 1, which serves as a hard constraint for the timing plan.

[0048] In addition to the common constraints mentioned above, other constraints may be encountered in actual applications, such as the range constraints of signal differences between adjacent intersections, the need for bus priority and emergency vehicle priority, etc. These constraints can be incorporated into the model as appropriate and assigned appropriate weight coefficients. At this point, a multi-constrained, nonlinear mixed integer programming model has been constructed. This model covers the main characteristics of road network traffic and can better guide the optimization solution of the timing plan.

[0049] S105, taking the signal light cycle, phase duration and phase difference of each intersection as timing parameters, and obtaining the optimal timing solution by iteratively calculating the solution of the timing strategy optimization model; After completing the construction of the timing optimization model, the system needs to use the optimization algorithm to solve the optimal solution of the model, that is, the optimal timing plan. This step uses the main parameters involved in the timing plan (signal light cycle, phase duration and phase difference) as decision variables, and uses iterative calculation to search for the optimal parameter combination, thereby obtaining the optimal timing strategy in a global sense.

[0050] In specific implementation, the system generally uses heuristic algorithms or intelligent optimization algorithms to solve the model. Such algorithms can obtain satisfactory solutions within an acceptable time and adapt to the nonlinear and multi-constrained characteristics of the model by repeatedly iterating to approach the optimal solution. For example, the genetic algorithm simulates the biological evolution process, performs operations such as selection, crossover, and mutation on a set of randomly generated initial solutions (i.e., timing parameter combinations), continuously generates new solutions with better performance, and finally converges to the optimal solution. Similarly, the ant colony algorithm simulates the behavior of ants looking for food, and guides the search path towards the global optimal solution through the accumulation and volatilization mechanism of pheromones. The key to these algorithms is to balance the quality of the solution and the convergence speed. The former requires extensive exploration in the search space, while the latter requires locking the promising area as soon as possible. By adjusting the relevant parameters of the algorithm, such as population size, number of iterations, crossover probability, pheromone concentration, etc., an appropriate trade-off can be found between the two goals.

[0051] In the process of searching for the optimal solution, the relevant parameters of the traffic lights at each intersection are taken into consideration as decision variables. Usually, the signal timing of adjacent intersections in an area needs to be coordinated with each other to achieve green wave traffic and reduce queuing and parking. Therefore, when optimizing, the intersections cannot be separated, but the road network should be optimized as a whole. Although this global coordinated timing method has a large amount of calculation, it can fully tap the potential traffic capacity of the road network and has more advantages than local optimization methods such as single-point control and coordinated control of main and secondary roads.

[0052] The urban road network is a dynamically changing system, and the calculated optimal timing solution can only remain optimal for a period of time. To adapt to new traffic needs, the system needs to re-execute the optimization process regularly and adjust the timing parameters. Therefore, a fast and efficient solution algorithm is an important guarantee for timing optimization. In addition to selecting intelligent algorithms with good performance, the system can also partition the road network and perform parallel calculations on each zone; it can discretize the timing parameters to reduce the solution space; it can prune during the calculation process to eliminate obvious inferior solutions as early as possible. These measures can help speed up the search for the optimal solution.

[0053] S106: Send the optimal timing plan to the traffic light controllers at each intersection, so that all traffic light controllers operate according to the optimal timing plan.

[0054] After successfully solving the optimal timing solution, the remaining work is to apply the solution to actual traffic light control, in order to form a coordinated and efficient signal timing across the entire road network and improve traffic efficiency. This step describes the implementation process of the optimal solution.

[0055] Specifically, the system first converts the optimal timing plan into a series of control instructions, mainly including parameters such as the cycle duration, phase sequence, and green light duration of each intersection. These instructions are generally encapsulated in standardized data formats, such as XML or JSON, to ensure that the information is complete and accurate during transmission and parsing. Then, the system sends the control instructions to the traffic light controllers at the corresponding intersections through wired or wireless communication networks (such as GPRS, 4G, NB-IoT, etc.). The controller is an embedded device deployed at the intersection, which directly controls the display of traffic lights through the I / O interface. It obtains timing instructions from the system, switches traffic lights strictly according to the specified duration, phase sequence and other parameters, and finally implements the timing plan.

[0056] In the above embodiment, by acquiring multi-source traffic data in real time and calculating the delay time based on the improved Webster delay formula, the actual traffic operation status of each intersection can be accurately reflected. After the traffic status of each intersection is divided into levels, a timing strategy optimization model with the minimum total delay time as the objective function is constructed. The model abstracts the urban road network into a graph structure and takes into account constraints such as the minimum green light time, the maximum green light time and the intersection saturation, so that the optimization process can comprehensively weigh the traffic needs of each intersection. By iteratively optimizing the signal light cycle, phase duration and phase difference as timing parameters, a timing scheme that minimizes the total network delay time under various constraints can be obtained. The timing scheme can adapt to the dynamic changes of urban traffic flow, improve the traffic efficiency of the road network, reduce the delay time of vehicles in the road network, and improve the quality of traffic operation.

[0057] In order to further improve the reliability and practicality of the above-mentioned timing strategy optimization method, the embodiment of the present application also provides a bottleneck section identification and optimization method based on historical timing data. This method analyzes the historical data of signal timing at each intersection in the road network, identifies sections that are prone to traffic bottlenecks, and uses reverse optimization to adjust the timing plan in advance, thereby preventing the occurrence of traffic congestion. Figure 2 , a bottleneck section identification and optimization method based on historical timing data in an embodiment of the present application is described: See also Figure 2 , which is a flow chart of a bottleneck section identification and optimization method based on historical timing data in an embodiment of the present application.

[0058] S201, constructing a road network periodic fluctuation curve based on historical timing data of each intersection; The system constructs a road network cycle fluctuation curve based on the historical timing data of each intersection. The road network cycle fluctuation curve reflects the changing rules of the signal cycle of each intersection in the road network, including: The sliding time window method is used to process the historical timing data of each intersection in sections; Wavelet transform is used to perform multi-scale decomposition on the data in each time window to extract the main frequency components of periodic changes; The periodic variation trend is reconstructed based on the main frequency components to generate a smooth periodic fluctuation curve of the road network.

[0059] This step analyzes the historical change data of the signal light timing schemes at each intersection in the road network to extract the long-term change rules of the signal cycle, laying the foundation for the subsequent identification of traffic bottlenecks and optimization of the timing scheme. Since actual road traffic has obvious time characteristics, such as morning and evening rush hours, differences between weekdays and weekends, etc., changes in signal cycles are often cyclical. Exploring this cyclical law can better characterize the dynamic characteristics of road network traffic, predict the changing trend of traffic volume in the future, and then adopt targeted signal timing strategies.

[0060] In the specific implementation, the system first uses the sliding time window method to segment the historical timing data. The sliding window divides the continuous time series data into several overlapping time segments by setting a fixed time span (such as 1 hour) and a sliding step size (such as 15 minutes), and each segment corresponds to an observation window. Then, the system performs multi-scale decomposition on the data in each window. Since the traffic cycle has different time granularities (such as hours, days, weeks, etc.), it is difficult for the traditional Fourier transform to fully extract such multi-scale features. Therefore, the system uses the wavelet transform method to project the window data onto different time-frequency scales by selecting different wavelet bases to obtain a series of wavelet coefficients that reflect the intensity of periodic changes at different scales. Finally, the system selects the wavelet coefficients with larger energy, reconstructs the main change trend of the signal cycle in the window, and connects the change trends of each window in chronological order to form a smooth and continuous road network periodic fluctuation curve.

[0061] S202, taking the local extreme point of the periodic fluctuation curve in the time dimension as the key time node; After successfully constructing the road network cycle fluctuation curve, the system needs to further determine the time nodes that have a significant impact on traffic conditions. This step uses the local extreme points of the fluctuation curve (i.e., local maximum and minimum points) as key nodes. These nodes often correspond to sudden changes in traffic conditions, such as the beginning and end of morning and evening rush hours, the occurrence and recovery of traffic accidents, etc. At these moments, the contradiction between road capacity and traffic volume is most prominent, and a slight carelessness may cause traffic bottlenecks. Therefore, it is necessary to focus on the coordination of the timing of the intersections before and after these nodes to predict the risk of traffic bottlenecks.

[0062] In specific implementation, the system uses numerical analysis methods to calculate the first-order derivative and second-order derivative of the road network periodic fluctuation curve. The change in the sign of the derivative reflects the monotonicity of the curve. The point where the first-order derivative is zero and the second-order derivative is less than zero is the local maximum point, and the point where the first-order derivative is zero and the second-order derivative is greater than zero is the local minimum point. For these extreme points, the system records their corresponding timestamps as key time nodes for subsequent analysis.

[0063] To ensure that the obtained extreme points do reflect the sudden changes in traffic conditions, the system can properly smooth the curves and remove extreme points caused by noise and minor disturbances. For example, an amplitude threshold is set for traffic flow fluctuations according to the road grade. When the amplitude change of two adjacent extreme points is less than the threshold, they are merged into the same extreme interval, and the point with the largest amplitude change in the interval is taken as the key node. In addition, the system can also combine expert experience to artificially set some key nodes with general rules, such as statutory holidays, major event periods, etc. Although such nodes may not be obvious in historical data, they are still of reference value for traffic prediction in the future.

[0064] S203, calculating the cycle difference between two adjacent intersections at a key time node; The system calculates the cycle difference between two adjacent intersections at key time nodes, specifically including: establishing the intersection signal timing feature vector, including the intersection signal timing feature vector cycle length, green-to-signal ratio, phase difference and flow distribution; The feature vector distance between the timing feature vectors of two adjacent intersections is calculated using a multi-dimensional feature distance calculation method based on improved cosine similarity; Calculate the road section saturation weight coefficient based on the road section capacity and actual demand; Multiply the eigenvector distance by the saturation weight coefficient to obtain the period difference.

[0065] After determining the key time nodes, the system needs to evaluate the timing differences between adjacent intersections at the nodes to predict the risk of traffic bottlenecks. This step introduces the concept of cycle difference, which is used to quantify the spatial deviation of the timing strategies of adjacent intersections at key nodes. The greater the difference, the more uncoordinated the timing of adjacent intersections is, and the more likely it is to induce a traffic bottleneck. By calculating the difference, an intuitive and measurable basis can be provided for subsequent bottleneck identification.

[0066] In the specific implementation, the system first establishes the feature vector of intersection signal timing. The feature vector contains multiple key parameters that affect the timing, such as signal cycle length, green-to-signal ratio (i.e., the proportion of green light duration), phase difference (i.e., the time difference between the start time of green lights at adjacent intersections), and traffic flow distribution. Then, the system uses the improved cosine similarity method to calculate the multidimensional distance between feature vectors. The improved cosine similarity not only considers the size of the features of each dimension, but also the correlation between different feature dimensions. For example, the cycle length is usually negatively correlated with the green-to-signal ratio, while the phase difference is positively correlated with the flow distribution. When calculating the distance, these features should be given different weights. Finally, the similarity measure is normalized to the interval [0, 1], and the resulting value is the cycle difference. The closer the value is to 1, the greater the difference in timing strategies.

[0067] In the calculation of the difference, the importance of each adjacent intersection is usually not the same. For intersections located on the main road with large traffic volume, the coordination of its timing plays a greater role in alleviating bottlenecks. Therefore, the system also needs to calculate the section weight and correct the difference. The calculation of the section weight needs to take into account multiple factors, such as the section's capacity, saturation, speed, queue length, etc. Among them, the capacity reflects the physical properties of the section, such as the number of lanes and lane width; the saturation reflects the flow level of the section, the larger the value, the more frequent the conflict; the speed reflects the driving conditions of the section, and if it is lower than the threshold, there may be delays; the queue length reflects the reserve capacity of the section, and if it exceeds the threshold, there is an overflow risk. The system can weight these factors and sum them to obtain the comprehensive weight of the section, and multiply it with the original difference to obtain the corrected period difference.

[0068] S204, when the cycle difference is greater than a preset threshold, marking the section between two adjacent intersections as a potential bottleneck section; Based on the cycle difference, the system can preliminarily identify potential bottleneck sections in the road network. This step sets the difference threshold. When the difference between adjacent intersections exceeds the threshold, it can be determined that the section connecting the two intersections is at risk of forming a traffic bottleneck, and then marked as a potential bottleneck section. The setting of the threshold needs to balance the accuracy and comprehensiveness of the identification on the basis of fully considering the actual traffic conditions of the road network. If the threshold is set too high, some bottleneck sections may be missed; if the threshold is set too low, some sections with little traffic pressure may be marked too much, affecting the targetedness of subsequent optimization.

[0069] In specific implementation, the system can use a hierarchical threshold method to set different difference thresholds for roads of different levels. For example, the thresholds for expressways and trunk roads are set to 0.6, the thresholds for secondary roads are set to 0.7, and the thresholds for branch roads are set to 0.8. When the difference between adjacent intersections exceeds the threshold corresponding to the level of the road they are on, the road section is marked as a potential bottleneck. The setting of hierarchical thresholds should be based on long-term traffic observation data and expert experience, and should be adapted to the traffic capacity of the road section and take into account the distribution characteristics of the bottlenecks of the entire road network. In practical applications, an adaptive threshold method can also be used to dynamically adjust the threshold according to the overall traffic conditions of the road network. For example, the threshold can be appropriately lowered during peak hours in the morning and evening to increase the sensitivity of bottleneck identification; the threshold can be appropriately increased at night when traffic is sparse to reduce the misjudgment rate.

[0070] In addition, due to random disturbances in road network topology and traffic flow, isolated bottleneck sections may not truly reflect traffic conditions, but may interfere with optimization decisions. Therefore, when identifying bottleneck sections, the system also needs to consider spatiotemporal correlation characteristics. For example, the mean difference of multiple consecutive sections on the same road can be calculated. When the mean exceeds the threshold, these sections are marked as potential bottlenecks as a whole. For another example, if a section is frequently marked as a bottleneck over a period of time, the weight of its bottleneck possibility should be increased. By integrating spatiotemporal features, the reliability of identifying potential bottleneck areas can be further improved.

[0071] S205. Optimize the reverse timing of potential bottleneck sections.

[0072] The system performs reverse timing optimization on potential bottleneck sections. The reverse timing optimization takes the upstream intersection as a benchmark and reversely adjusts the timing parameters of the downstream intersection to match the capacity of the potential bottleneck section with the traffic volume.

[0073] After identifying potential bottleneck sections, the system needs to optimize the timing plans of these sections in a targeted manner to alleviate or avoid traffic bottlenecks. Traditional timing optimization mostly adopts the idea of ​​forward adjustment, that is, starting from the starting intersection, adjusting the timing of each intersection along the direction of traffic flow to adapt it to upstream vehicles as much as possible. Although this idea is intuitive, it has certain limitations: when the traffic capacity of the downstream section cannot meet the traffic demand of the upstream, no matter how the timing is adjusted, it cannot fundamentally alleviate the bottleneck. Therefore, this step proposes a new idea of ​​reverse timing optimization, starting from the downstream intersection of the bottleneck section, and adjusting the timing of the upstream intersection in reverse to make it coordinated with the traffic capacity of the downstream section. This method is equivalent to governing the bottleneck source, which is more conducive to fundamentally alleviating congestion.

[0074] In specific implementation, the system first identifies the downstream key intersections (such as intersections, entrances and exits) of potential bottleneck sections and evaluates the traffic capacity of the intersections, such as the number of passable vehicles and the number of vehicles in queue. Then, the system sets the initial timing plan at the key intersections based on the actual traffic flow demand, and the main parameters include traffic capacity, saturation, delay time, etc. Next, the system starts from the downstream intersection and reversely adjusts the timing parameters of each upstream intersection, mainly including cycle length, green light ratio and phase difference. The goal of the adjustment is to make the traffic demand and traffic capacity between the upstream and downstream intersections match as much as possible, and the cycle difference is reduced to below the threshold. The adjustment process must follow a variety of constraints, such as cycle length range, minimum green light time, pedestrian crossing demand, etc. At the same time, it is also necessary to coordinate the timing plans of adjacent roads to avoid local adjustments causing global shocks. Optimization algorithms such as genetic algorithms and particle swarm algorithms can be used to search for the best timing parameter combination under constraints.

[0075] The key to reverse timing optimization is the estimation of the capacity of the road section. Since actual roads often exist in a supersaturated state, the capacity of the road section depends not only on the physical properties, but also on the upstream and downstream traffic conditions. Therefore, when evaluating the capacity, the system can adopt a dynamic estimation method to consider factors such as the traffic volume, queue length, and delay time of the upstream and downstream sections in real time. For example, when the traffic volume of the downstream section is large, the estimated capacity of the current section should be appropriately reduced; when there are many vehicles queuing in the upstream section, the capacity should also be lowered to reduce the queuing pressure upstream. The dynamic capacity combined with real-time traffic parameters can more accurately characterize the capacity constraints of the section, thereby obtaining a more robust timing optimization result.

[0076] In the above embodiment, by constructing the road network period fluctuation curve, the dynamic change law of the signal period of each intersection in the road network can be accurately reflected, and combined with the calculation of the period difference of the key time nodes, the potential traffic bottleneck section can be identified in time. The identified potential bottleneck section is reversely optimized, and the timing parameters of the downstream intersection are reversely adjusted based on the upstream intersection, so that the traffic capacity of the potential bottleneck section matches the actual traffic volume, which can prevent the generation and spread of traffic congestion and improve the overall traffic efficiency of the road network.

[0077] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of a city-level timing strategy optimization system provided in an embodiment of the present application.

[0078] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0079] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0080] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0081] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0082] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0083] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0084] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0085] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0086] As used in the above embodiments, the term "when..." may be interpreted as "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted as "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0087] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0088] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A city-level timing strategy optimization method, characterized in that: include: Obtain the traffic flow, speed, vehicle type, queue length and lane occupancy of each intersection collected by multi-source traffic data collection equipment in real time to obtain the original traffic data; Calculating the vehicle delay time at each intersection in different directions using an improved Webster delay formula based on the original traffic data; Classifying the traffic status of each intersection into unblocked, lightly congested, moderately congested and severely congested according to the vehicle delay time of each intersection in different directions; Based on the traffic state, a timing strategy optimization model with the minimum total delay time as the objective function is constructed, and the urban road network is abstracted into a graph structure, in which each intersection is a node and the road section is an edge. The constraint conditions of the timing strategy optimization model include a minimum green light time constraint, a maximum green light time constraint and an intersection saturation constraint; The signal light cycle, phase duration and phase difference of each intersection are used as timing parameters, and the optimal timing solution is obtained by iteratively calculating the solution of the timing strategy optimization model; The optimal timing scheme is sent to the signal light controllers at each intersection, so that all the signal light controllers operate according to the optimal timing scheme.

2. The method according to claim 1, characterized in that The method of calculating the vehicle delay time of each intersection in different directions by using the improved Webster delay formula based on the original traffic data specifically includes: Preprocessing the original traffic data to obtain preprocessed traffic data, wherein the preprocessing includes removing abnormal data that exceeds a preset range; Dividing the pre-processed traffic data according to time windows, and calculating the average vehicle arrival rate and average service time of each intersection in each time window; The average vehicle arrival rate and the average service time are substituted into the improved Webster delay formula to calculate the vehicle delay time of each intersection in different directions.

3. The method according to claim 1, characterized in that The traffic status of each intersection is divided into smooth traffic, light congestion, moderate congestion and severe congestion according to the vehicle delay time of each intersection in different directions, specifically including: The average delay time of each intersection in different time periods obtained by statistics is used as a benchmark threshold; Calculating the deviation rate between the real-time delay time of each intersection and the benchmark threshold value using the benchmark threshold value; Constructing a state evaluation index matrix according to the deviation rate, and performing preliminary clustering using the state evaluation index matrix to obtain a clustering result; The traffic status of each intersection is divided into unblocked, lightly congested, moderately congested and severely congested according to the clustering result.

4. The method according to claim 1, characterized in that: The timing strategy optimization model based on the traffic state is constructed with the minimum total delay time as the objective function, specifically including: Calculating the traffic capacity coefficient of each road section based on the traffic status of each intersection; Establishing a road network connectivity constraint matrix, wherein the element values ​​of the road network connectivity constraint matrix are determined according to the distance between adjacent intersections, the vehicle flow arrival rate and the green wave belt speed; According to the capacity coefficient and the road network connectivity constraint matrix, the total delay time is expressed as a function of the signal timing parameters of each intersection, wherein the function includes a random delay term and a uniform delay term, wherein the random delay term is determined by the randomness of vehicle arrival, and the uniform delay term is determined by the signal timing scheme; Establishing mathematical expressions of a minimum green light time constraint, a maximum green light time constraint, and an intersection saturation constraint, wherein the minimum green light time constraint is determined according to pedestrian crossing demand, the maximum green light time constraint is determined according to the length of a vehicle queue, and the intersection saturation constraint is determined according to traffic capacity; The function is used as the objective function, and the minimum green light time constraint, the maximum green light time constraint and the intersection saturation constraint are used as constraint conditions to obtain a timing strategy optimization model.

5. The method according to claim 1, characterized in that After sending the optimal timing scheme to the traffic light controllers at each intersection, the method further includes: Constructing a road network period fluctuation curve based on the historical timing data of each intersection, wherein the road network period fluctuation curve reflects the variation law of the signal period of each intersection in the road network; Taking the local extreme point of the periodic fluctuation curve in the time dimension as the key time node; Calculating the cycle difference between two adjacent intersections at the key time node; When the period difference is greater than a preset threshold, marking the road section between two adjacent intersections as a potential bottleneck section; The potential bottleneck section is subjected to reverse timing optimization, wherein the reverse timing optimization is to adjust the timing parameters of the downstream intersection inversely based on the upstream intersection, so that the traffic capacity of the potential bottleneck section matches the traffic volume.

6. The method according to claim 5, characterized in that The step of constructing a road network periodic fluctuation curve based on the historical timing data of each intersection specifically includes: The historical timing data of each intersection is processed in sections by using a sliding time window method; Wavelet transform is used to perform multi-scale decomposition on the data in each time window to extract the main frequency components of periodic changes; The periodic variation trend is reconstructed based on the main frequency components to generate a smooth road network periodic fluctuation curve.

7. The method according to claim 5, characterized in that Calculating the cycle difference between two adjacent intersections at the key time node specifically includes: Establishing a traffic signal timing feature vector, which includes a traffic signal timing feature vector cycle length, green-to-signal ratio, phase difference and flow direction distribution; The feature vector distance between the timing feature vectors between two adjacent intersections is calculated using a multi-dimensional feature distance calculation method based on improved cosine similarity; Calculate the road section saturation weight coefficient based on the road section capacity and actual demand; The characteristic vector distance is multiplied by the saturation weight coefficient to obtain the period difference.

8. A city-level timing strategy optimization system, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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