Intelligent Transportation Dynamic Green Wave Control Method, System, Storage Medium and Program Product

Abstract

The present application provides an intelligent transportation dynamic green wave control method, system, storage medium and program product, which relates to the field of traffic control systems. The method includes: obtaining the real-time traffic data of each lane and the emergency data announced by the traffic management department; and based on the neural network model, obtaining the traffic flow prediction results in different directions of each road section within a preset future time; then combining the emergency data and the traffic flow theory model, outputting the congestion level and congestion duration of each road section, and further determining the adjustment sequence; and successively based on the traffic flow prediction results, using the particle swarm optimization algorithm to calculate the optimal green wave bandwidth of the traffic lights at each intersection within each target road section, obtaining the optimal timing combination scheme of the traffic lights within each target road section, and performing real-time timing control. Implementing this method can more efficiently guide traffic flow, reduce the number of vehicle stops and delay time on the road, improve the overall traffic capacity of urban roads, and reduce energy consumption and environmental pollution.

Description

Technical Field

[0001] The present application relates to the field of traffic control systems, and in particular to an intelligent traffic dynamic green wave control method, system, storage medium and program product. Background Art

[0002] In recent years, with the acceleration of urbanization and the continuous increase in the number of motor vehicles, urban traffic congestion has become increasingly serious, posing a significant challenge to people's daily travel and the efficiency of urban operations. To alleviate urban traffic pressure, various intelligent traffic management technologies have been widely applied. Among them, green wave control technology, as an effective traffic signal coordination and control method, can optimize traffic light timing, reduce the number of vehicle stops and waiting time during driving, improve traffic efficiency, and reduce fuel consumption and exhaust emissions. It has become a key research direction in the field of traffic management.

[0003] Related technologies typically develop signal control plans based on fixed historical traffic data or pre-defined traffic flow patterns. For example, some green wave control systems perform statistical analysis of traffic flow on a road section, taking into account the road's geometry, signal layout, and vehicle speeds to pre-determine fixed green wave bandwidths and signal timings. These systems can, to a certain extent, meet traffic demand within a specific time period, reduce congestion, and improve the overall capacity of the road network.

[0004] However, related technologies mainly rely on historical data or simple statistical laws to formulate signal timing plans, which are difficult to cope with the dynamic changes in real-time traffic flow. Especially when sudden traffic conditions (such as traffic accidents, bad weather or temporary control) occur, the existing plans lack flexibility and it is difficult to effectively adjust traffic signals, resulting in low road traffic efficiency. Summary of the Invention

[0005] The present application provides an intelligent traffic dynamic green wave control method, system, storage medium and program product for solving the problem of how to optimize the timing of urban road traffic lights and improve road traffic efficiency.

[0006] In a first aspect, the present application provides an intelligent traffic dynamic green wave control method, which is applied to a traffic green wave control system, and is characterized in that the method includes:

[0007] Obtaining real-time traffic data for each lane and emergency data published by traffic management departments. The real-time traffic data is collected by sensors installed at intersections;

[0008] Input the pre-processed real-time traffic data into a neural network model trained based on historical traffic data and relevant influencing factor data, and output traffic flow prediction results for different directions of each road section within a preset time in the future;

[0009] Input the traffic flow prediction result and the emergency data into a traffic flow theory model, and output the congestion level and congestion duration of each road section in different directions;

[0010] Determining the adjustment priorities of different directions of target road sections that meet preset conditions according to the congestion level and congestion duration, and obtaining adjustment sequences of different directions of each target road section;

[0011] According to the corresponding order of the adjustment sequence, based on the traffic flow prediction results, the particle swarm optimization algorithm is used to calculate the optimal green wave bandwidth of the traffic lights at each intersection in each target road section, so as to obtain the optimal timing combination scheme of the traffic lights in each target road section;

[0012] The optimal timing combination scheme is sent to the traffic signal light controller for real-time timing control.

[0013] Through the above embodiments, the traffic green wave control system dynamically adjusts the green wave parameters of traffic lights at each intersection within the target road section according to real-time traffic flow, road conditions and other relevant factors, so as to achieve more efficient traffic flow diversion, reduce the number of vehicle stops and delays on the road, improve the overall traffic capacity of urban roads, and reduce energy consumption and environmental pollution.

[0014] In some embodiments, the step of calculating the optimal green wave bandwidth of the traffic lights at each intersection in each target road section using a particle swarm optimization algorithm based on the traffic flow prediction result to obtain the optimal timing combination scheme of the traffic lights in each target road section specifically includes:

[0015] Determining the constraint conditions of the particle swarm optimization algorithm based on the traffic flow prediction result, the phase difference between the traffic lights at each intersection and the preset saturation flow;

[0016] The objective function of the particle swarm optimization algorithm is constructed based on minimizing the average vehicle delay time, the number of stops and the maximum queue length;

[0017] The optimal green wave bandwidth is calculated according to the constraint conditions and the objective function to obtain the optimal timing combination scheme.

[0018] Through the above embodiment, the traffic green wave control system sets constraints and objective functions during the optimization process, with the optimization goal of minimizing the average delay time, number of stops and maximum queue length of vehicles, thereby achieving precise adjustment of traffic signal timing, improving the traffic capacity of the entire road network, and reducing traffic pressure during peak hours.

[0019] In some embodiments, before the step of determining the constraint conditions of the particle swarm optimization algorithm based on the traffic flow prediction result, the phase difference between the traffic lights at each intersection, and the preset saturation flow, the method further includes:

[0020] Obtain the first distance values between each intersection within the target road section;

[0021] Calculate the average speed based on the historical driving speeds of the vehicles between each intersection within the target road section;

[0022] Determine the phase difference between the traffic lights at each intersection within the target road section based on the first distance value and the corresponding average speed.

[0023] Through the above embodiments, the traffic green wave control system determines the phase difference between the traffic lights by calculating the average speed and the corresponding distance value, can accurately match the actual traffic conditions, and enables vehicles to pass through the target road section at a more efficient speed. At the same time, this technology combines the actual operating speed of the vehicles, can better reduce the number of stops and delay time, and improves the driving efficiency.

[0024] In some embodiments, the step of determining the phase difference between the traffic lights at each intersection within the target road section based on the first distance value and the corresponding average speed specifically includes:

[0025] Determine the minimum phase difference corresponding to the shortest road section based on the first distance value and the average speed;

[0026] Determine the other phase differences of the other road sections based on the ratio of the first distance values between the other road sections within the target road section and the shortest road section, and the other phase differences are integer multiples of the minimum phase difference.

[0027] Through the above embodiments, the traffic green wave control system is based on the actual length and speed data of the road, ensures that the phase difference between the traffic lights can meet the requirements of continuous vehicle passing, and minimizes the parking frequency and delay time of the vehicles within the target road section to the greatest extent. Through this hierarchical phase difference adjustment method, it can better adapt to the diversity of each road section in the road network and improve the overall effect of the green wave control.

[0028] In some embodiments, before the step of constructing the objective function of the particle swarm optimization algorithm based on minimizing the average vehicle delay time, the number of stops, and the maximum queue length, the method further includes:

[0029] Determine the peak phases according to the congestion level and congestion duration, and the peak phases include the peak period and the off-peak period;

[0030] If the peak stage is the peak period, set the first preset length threshold as the maximum queuing length in the objective function. The maximum queuing length includes the first preset length threshold and the second preset length threshold, and the first preset length threshold is less than the second preset length threshold.

[0031] If the peak stage is the off-peak period, set the first preset number threshold as the number of parking times in the objective function. The number of parking times includes the first preset number threshold and the second preset number threshold, and the first preset number threshold is less than the second preset number threshold.

[0032] Through the above embodiments, the traffic green wave control system sets the maximum queuing length as the optimization parameter of the objective function for the peak period, which can effectively relieve the congestion during the traffic peak. During the off-peak period, by optimizing and adjusting the number of parking times, the vehicle passing efficiency is further improved. This dynamic phased optimization method effectively adapts to the requirements under different traffic flow conditions, enabling the green wave control scheme to achieve the best effect in each stage. At the same time, based on the flexible adjustment of the objective function, the signal timing scheme can be more accurately optimized, reducing the parking and delay time of vehicles, improving the passing efficiency, and reducing fuel consumption and carbon emissions.

[0033] In some embodiments, before the step of determining the adjustment priorities of different directions of the target road section that meets the preset conditions according to the congestion level and congestion duration, it further includes:

[0034] Obtain the second distance values between each intersection.

[0035] Determine the road section where a continuous preset number of the second distance values are less than or equal to the preset distance threshold as the target road section that meets the preset conditions.

[0036] Through the above embodiments, the traffic green wave control system calculates the distances between intersections and determines the continuous road sections that meet the preset conditions as the target road sections. This method can better focus on optimizing and adjusting the road sections with greater traffic pressure. It can avoid resource dispersion in unnecessary areas, improving the pertinence and priority of signal optimization. At the same time, the dynamic target road section screening mechanism can also adapt to the changes in traffic flow in real time, making the green wave control system more flexible and intelligent, and improving the operation efficiency of the entire road network while alleviating traffic congestion.

[0037] In some embodiments, after the step of sending the optimal timing combination scheme to the traffic signal controller for real-time timing control, it further includes:

[0038] Determine the guiding vehicle speed between the adjacent intersections according to the optimal timing combination scheme and the distance between the adjacent intersections within the target road section.

[0039] The guide speed is sent to the mobile terminal of the driver at the corresponding position through the intelligent traffic guidance system for voice prompting.

[0040] Through the above-described embodiment, the guided speed between adjacent intersections within a target road section is determined by the optimal timing combination and the intersection distance. This speed is then transmitted in real time to the driver's mobile device via the intelligent traffic guidance system. This approach effectively guides drivers through green wave sections at a reasonable speed, avoiding stops or delays caused by speed mismatches. Furthermore, through voice prompts, drivers can receive more intuitive driving guidance information, reducing decision-making delays and improving driving safety and comfort.

[0041] In a second aspect, the present application provides a traffic green wave control system, the traffic green wave control system comprising: one or more processors and a memory;

[0042] The memory is coupled to the one or more processors, and the memory is used to store computer program code, wherein the computer program code includes computer instructions. The one or more processors call the computer instructions so that the traffic green wave control system can implement an intelligent traffic dynamic green wave control method provided in the above embodiment, which will not be repeated here.

[0043] On the third aspect, the present application provides a computer-readable storage medium, including instructions, characterized in that when the instructions are run on a traffic green wave control system, the traffic green wave control system can implement an intelligent traffic dynamic green wave control method provided in the above embodiment, which will not be repeated here.

[0044] Fourthly, the present application provides a computer program product. When the computer program product runs on a traffic green wave control system, the traffic green wave control system can implement an intelligent traffic dynamic green wave control method provided in the above embodiment, which will not be repeated here.

[0045] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0046] 1. By integrating real-time traffic data, historical traffic data, and emergency data, combined with neural network models and traffic flow theory models, this invention achieves accurate prediction and dynamic adjustment of future traffic flows. Compared to traditional green wave control methods that rely on fixed historical data, this invention significantly improves control flexibility and adaptability. In particular, in response to emergencies such as traffic accidents and severe weather, it can quickly adjust signal timing plans, reducing congestion time, improving road traffic efficiency, and effectively reducing vehicle fuel consumption and pollution emissions.

[0047] 2. By introducing the particle swarm optimization algorithm and combining it with the traffic flow prediction results of the target section, the green wave bandwidth and timing combination scheme of the traffic lights are dynamically optimized. At the same time, the phase difference of the traffic lights is adjusted using the road section distance and vehicle speed data. This optimization method based on constraint conditions and objective functions significantly reduces the vehicle delay time, stop times, and queue length, improves the accuracy of green wave control and the dynamic response ability of the system, adapts to complex traffic flow changes, and significantly improves the traffic efficiency and traffic fluency of the target section.

[0048] 3. By integrating the dynamic calculation of the guiding vehicle speed and the intelligent transportation guidance system, the green wave control effect is further optimized and the driving experience is improved. The guiding vehicle speed is generated in real time according to the traffic light timing scheme and the intersection spacing, and is transmitted to the driver through voice prompts, effectively guiding the vehicle to pass through the target section at the best speed and avoiding stops and delays. This not only improves the adaptability of green wave control in a dynamic traffic environment, but also strengthens the interaction between people and vehicles through intelligent means, significantly improving driving safety and traffic efficiency, and at the same time reflecting the intelligent development direction of the traffic management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flowchart of an intelligent traffic dynamic green wave control method in an embodiment of the present application;

[0050] Figure 2 is another flowchart of an intelligent traffic dynamic green wave control method in an embodiment of the present application;

[0051] Figure 3 is a schematic structural diagram of an entity device of a traffic green wave control system in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] 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 intended to include the plural forms as well, unless the context clearly indicates 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.

[0053] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and should not be construed as implying or indicating 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.

[0054] For ease of understanding, the following describes the process of the method provided by this implementation. Figure 1 , which is a flow chart of an intelligent traffic dynamic green wave control method in an embodiment of the present application.

[0055] S101. Obtain real-time traffic data for each lane and emergency data published by the traffic management department.

[0056] Traffic data is collected in real time through a variety of sensor devices deployed at intersections. These include, but are not limited to: video detectors capture images of passing vehicles and identify information such as traffic volume, vehicle type, and speed; geomagnetic detectors buried beneath the road surface detect magnetic field changes generated by passing vehicles and calculate traffic volume and occupancy; and microwave radar detectors mounted on streetlights or signal poles provide 24 / 7 monitoring of vehicle movement. The data collected by these sensors is pre-processed and uploaded to the traffic control center. The system also accesses real-time data released by traffic management authorities regarding emergencies such as traffic accidents, road construction, major events, and severe weather.

[0057] S102: Input the pre-processed real-time traffic data into a neural network model trained based on historical traffic data and relevant influencing factor data, and output traffic flow prediction results for different directions of each road section within a preset time in the future.

[0058] The traffic green wave control system preprocesses collected real-time traffic data, including outlier handling, data standardization, and time alignment. The processed data is then fed into a pre-trained deep neural network model. Optionally, this deep neural network model utilizes a long short-term memory (LSTM) architecture, trained on a large amount of historical traffic data. Optionally, the training data includes multi-dimensional information such as traffic flow data from the past three years, weather data, holiday information, and records of major events. Through training, the deep neural network model captures the spatiotemporal correlations and cyclical variations in traffic flow.

[0059] In one specific embodiment, the traffic green wave control system uses the most recent 30-minute traffic flow time series data, historical data for the current period, weather conditions, date type, and other characteristics. After processing through a multi-layer neural network, it outputs traffic flow forecasts for each road section within the next 15-30 minutes. The model uses a sliding time window approach, updating the forecast results every 5 minutes.

[0060] S103: Input the traffic flow prediction results and emergency data into the traffic flow theory model, and output the congestion level and congestion duration of each road section in different directions.

[0061] Specifically, the traffic green wave control system inputs the future traffic flow forecast output by the neural network model in step S102, along with real-time emergency data (such as traffic accidents, road construction, and major events) released by traffic management departments, into a traffic flow theory model for analysis. The model then outputs the congestion level and duration for each road section and direction. Traffic flow theory models are a type of mathematical model used to describe the operating patterns of traffic flow and reveal the underlying mechanisms of traffic phenomena. They include macroscopic traffic flow models (such as the LWR model and the PW model) and microscopic traffic flow models (such as the car-following model and the lane-changing model).

[0062] In one specific example, consider an east-west road where traffic flow forecasts indicate a continued increase in eastbound traffic over the next 30 minutes, coupled with construction that reduces the eastbound two-lane traffic flow to a single lane. The traffic green wave control system inputs this information into a theoretical traffic flow model. By calculating the road's capacity and traffic demand ratio, it outputs a congestion level of "congested" for the eastbound direction and an estimated congestion duration of "45 minutes," while the westbound direction remains "unimpeded."

[0063] It can be understood that the congestion level reflects the traffic operation status of each road section, which is usually divided into multiple levels such as smooth, slow, congested, and severe congestion; and the congestion duration indicates the expected length of time the congestion situation will last from the current moment.

[0064] S104 , determining the adjustment priorities of different directions of target road sections that meet preset conditions according to the congestion level and the congestion duration, and obtaining adjustment sequences of different directions of each target road section.

[0065] After obtaining the congestion levels and durations in different directions of each road section, the traffic green wave control system selects sections that meet the preset conditions as optimization targets, and determines the priority of these target sections in signal timing adjustment, namely the adjustment priority and adjustment sequence.

[0066] Specifically, the traffic green wave control system selects sections that meet the conditions of congestion level and congestion duration from all sections as optimization targets according to pre-set screening rules. Optionally, the screening conditions include, but are not limited to: the congestion level reaches "congested" or "severely congested", the congestion duration exceeds a certain threshold (such as 30 minutes), located in key areas or sensitive sections, etc. Then, the traffic green wave control system further analyzes the adjustment priorities of these target sections in different directions. This priority determines the degree of attention and adjustment intensity of the system to different directions of each section in the subsequent signal timing optimization. Optionally, the priority judgment rules are as follows: the direction with a higher congestion level and a longer congestion duration has a higher priority; the key guarantee direction (such as the bus lane) has a higher priority; the entrance and exit directions of important areas (such as commercial areas, transportation hubs) have a higher priority, etc. The system comprehensively evaluates and ranks the priorities of each section direction based on various factors, which is not limited here.

[0067] Finally, based on the above analysis results, the traffic green wave control system generates adjustment sequences for different directions of each target section. Among them, the adjustment sequence stipulates in what order the system adjusts the green wave parameters of each direction of each target section during the signal timing optimization process. The generation of the sequence follows the principle of high to low priority, ensuring that under the condition of limited optimization resources, the needs of the sections and directions with the greatest congestion pressure can be preferentially met. At the same time, the setting of the sequence also provides an adjustment plan for the green wave coordinated control, reducing mutual influence and conflicts.

[0068] In a specific embodiment, assume that three sections, namely A, B, and C, are selected as optimization targets. Among them, both the east and west directions of section A are "congested", the south direction of section B is "severely congested" and the north direction is "smooth", and the east direction of section C is "congested" and the west direction is "slow". After priority analysis, the adjustment sequence obtained by the system is: south direction of section B > east direction of section A > west direction of section A > east direction of section C > west direction of section C > north direction of section B. This sequence ensures that the direction of the section with the most severe congestion can be adjusted first.

[0069] S105. According to the corresponding order of the adjustment sequence, based on the traffic flow prediction results in turn, use the particle swarm optimization algorithm to calculate the optimal green wave bandwidth of the signal lights at each intersection within each target section, and obtain the optimal signal timing combination plan for the signal lights within each target section.

[0070] After determining the optimization order for the target road sections, the traffic green wave control system performs signal timing optimization calculations for each section. Specifically, the particle swarm optimization algorithm is used to determine the optimal green wave bandwidth and phase difference for traffic lights within each section, thereby obtaining the optimal signal timing combination for each section. The particle swarm optimization (PSO) algorithm is a heuristic optimization algorithm based on swarm intelligence. It simulates the foraging behavior of bird flocks to search for the optimal solution in the solution space. In green wave control, the particle swarm algorithm treats the green wave parameters of each intersection signal light (such as green-to-signal ratio, phase difference, etc.) as a "particle," and the combination of parameters across multiple intersections forms a "particle swarm." Through iterative optimization, the algorithm enables the particle swarm to continuously search the solution space, ultimately converging on the global optimal solution—the optimal signal timing combination.

[0071] Specifically, the traffic green wave control system sets the constraints of the particle swarm algorithm based on the traffic flow prediction results, combined with parameters such as the distance between intersections and road speed limits. These constraints ensure that the solution space searched by the algorithm meets the physical limitations and safety requirements of actual road traffic, such as the coordination of green wave bandwidths between adjacent intersections and the reasonable range of signal cycles. Then, the system constructs the fitness function of the particle swarm algorithm based on the optimization objectives as a criterion for judging the quality of particles. In green wave control, optional optimization objectives include but are not limited to minimizing vehicle delay time, number of stops, and queue length. The fitness function quantifies these objectives into computable indicators through mathematical modeling, and sets corresponding weight coefficients to balance the importance of different objectives.

[0072] After setting the algorithm parameters, the green wave control system executes the particle swarm optimization algorithm on each target road section in the order of the adjustment sequence. The algorithm iteratively searches to evaluate the fitness of different signal timing combinations and dynamically adjusts the particle speed and position based on the particle's historical and global optimal solutions, ultimately converging on the optimal green wave bandwidth combination. This combination best meets the optimization objectives and ensures fast and smooth vehicle movement within the road section.

[0073] S106: Send the optimal timing combination plan to the traffic signal controller for real-time timing control.

[0074] Specifically, the system converts the optimal timing combination plan into timing data in a standard format recognizable by the controller, including but not limited to parameters such as the duration of each signal light phase, phase sequence, repetition period, etc. Then, the traffic green wave control system distributes the timing data to the signal controllers at the corresponding intersections through a wired or wireless communication network using a dedicated communication protocol (such as NTCIP, SCATS, etc.). After receiving the data, the controller checks its integrity and legality according to the established process to ensure that the timing plan can be executed safely and reliably. The timing data that passes the check is written into the storage unit of the controller and becomes the active control plan.

[0075] In the above embodiment, the traffic green wave control system dynamically adjusts the green wave parameters of the signal lights at each intersection within the target section according to the real-time traffic flow, road conditions, and other relevant factors, so as to achieve more efficient traffic flow guidance, reduce the number of stops and delay time of vehicles on the road, improve the overall traffic capacity of urban roads, and reduce energy consumption and environmental pollution.

[0076] The following is a further and more specific process description of the method provided in this embodiment. Please refer to Figure 2 , which is another process schematic diagram of an intelligent traffic dynamic green wave control method in the embodiment of the present application.

[0077] S201. Obtain the second distance values between each intersection.

[0078] The traffic green wave control system first obtains the distance data between each intersection in the road network, that is, the second distance values. These distance data can be obtained in various ways. For example, by using a high-precision electronic map database, which contains detailed geometric structure information and intersection coordinate positions of the road network; or by field measurement, construction drawings of the road department, etc. to obtain the distance data, which is not limited here.

[0079] S202. Determine the target section that meets the preset conditions for the section where the continuous preset number of second distance values is less than or equal to the preset distance threshold.

[0080] Specifically, the traffic green wave control system sets two parameters according to actual needs: the preset number of consecutive intersections and the distance threshold. In a sliding window manner, it traverses all possible sections in the road network. For each section, all the intersections it contains are extracted, and it is calculated whether the distances between adjacent intersections all meet the threshold requirements, and it is judged whether the number of consecutive intersections that meet the requirements reaches the preset number. If both conditions are met, the section is added to the candidate list of the target section.

[0081] In the above embodiments, the traffic green wave control system determines the target road sections by calculating the distances between intersections and identifying consecutive road sections that meet the preset conditions. This method can better focus on optimizing and adjusting the road sections with relatively high traffic pressure, avoid resource dispersion in unnecessary areas, and enhance the pertinence and priority of signal optimization. At the same time, the screening mechanism for dynamic target road sections can also adapt to changes in traffic flow in real time, making the green wave control system more flexible and intelligent. While alleviating traffic congestion, it improves the operation efficiency of the entire road network.

[0082] S203. Determine the adjustment priorities for different directions of the target road sections based on the congestion level and duration, and obtain the adjustment sequences for different directions of each target road section.

[0083] This step is the same as step S104 and will not be elaborated here.

[0084] S204. Obtain the first distance values between the intersections within the target road sections.

[0085] The system obtains the distance data between the intersections within the target road sections, that is, the first distance values. Different from the second distance values at the road network level obtained in step S201, the acquisition of the first distance values is more focused on a single target road section and belongs to refined data collection.

[0086] Furthermore, the method for obtaining the first distance values is similar to that of the second distance values and can be obtained through methods such as querying the high-precision map database and on-site measurement, which is not limited here. The system inputs the collected coordinate positions of each intersection into the distance calculation model to generate a distance matrix focused on the target road section.

[0087] S205. Calculate the average speed based on the historical driving speeds of the vehicles between the intersections within the target road sections.

[0088] Specifically, the traffic green wave control system will access the historical database of the road monitoring system and extract the vehicle speed records between the intersections within the target road sections. These records can come from multiple channels such as video monitoring devices, vehicle detectors, and Internet map navigation platforms within the road section. The system classifies and summarizes the speed data for multiple consecutive days (such as one week) according to different time periods (such as morning and evening rush hours and off-peak hours), and then uses statistical methods to calculate the average value of the driving speeds between the intersections.

[0089] S206. Determine the minimum phase difference corresponding to the shortest road section based on the first distance values and the average speed.

[0090] Specifically, the traffic green wave control system comprehensively analyzes the intersection distance matrix and the average speed matrix. First, find the pair of adjacent intersections with the shortest distance in the distance matrix as the reference section for calculating the minimum phase difference. Then, access the corresponding vehicle speed value of this section in the average speed matrix and substitute it into the formula "phase difference = distance ÷ speed" to obtain the minimum phase difference. It should be noted that since the average speed matrix contains data for multiple time periods, the speed value during the off-peak period is generally selected when calculating the minimum phase difference to reflect the vehicle speed level under normal traffic conditions within the section.

[0091] S207. Determine the other phase differences of other sections based on the ratio of the first distance value between other sections and the shortest section within the target section, where the other phase differences are integer multiples of the minimum phase difference.

[0092] Specifically, the system extracts the first distance values between all other pairs of adjacent intersections within the target section, and then calculates the ratio of these distance values to the distance of the shortest section. The ratio result is usually a real number greater than 1. Considering that the phase difference must be an integer multiple of the minimum phase difference, the system performs rounding processing on the above ratio. Optional rounding strategies include rounding up, rounding down, and rounding to the nearest integer, etc., which are not limited here.

[0093] In some embodiments, assume that the distance of the shortest section J1-J2 is 200 meters, and the corresponding minimum phase difference is 20 seconds. Now consider another pair of adjacent intersections J3-J4 with a distance of 350 meters between them. The distance ratio of the two sections is 350÷200 = 1.75. After rounding, it gets 2, that is, the phase difference of J3-J4 should be set to 2 times the minimum phase difference, which is 40 seconds. Similarly, the phase difference schemes for all other intersections within the target section can be calculated.

[0094] In the above embodiments, the traffic green wave control system, based on the actual road length and speed data, ensures that the phase difference between signal lights can meet the requirements of continuous vehicle passing, and minimizes the parking frequency and delay time of vehicles within the target section. Through this hierarchical phase difference adjustment method, it can better adapt to the diversity of each section in the road network and improve the overall effect of green wave control.

[0095] S208. Determine the constraint conditions of the particle swarm optimization algorithm based on the traffic flow prediction result, the phase difference between signal lights at each intersection, and the preset saturation flow.

[0096] S209. If the peak stage is the peak period, set the first preset length threshold as the maximum queue length in the objective function.

[0097] Specifically, the system determines the peak stage to which the current period belongs in real time based on the congestion level and congestion duration. If it is the peak period, the first preset length threshold with a lower value is selected as the maximum queue length in the objective function, and the queue length is controlled at the lowest level as much as possible, that is, not exceeding the first preset length threshold. This threshold selection strategy can effectively cope with the traffic pressure during the peak period, prevent the excessive accumulation of queuing vehicles, quickly disperse the traffic flow, and improve the traffic efficiency.

[0098] It should be noted that the first preset length threshold is less than the second preset length threshold, and the specific value can be determined according to actual needs and is not limited here.

[0099] S210. If the peak stage is the off-peak period, the first preset number threshold is set as the number of stops in the objective function.

[0100] Specifically, the system determines the peak stage to which the current period belongs in real time based on the congestion level and congestion duration. If it is the off-peak period, the first preset number threshold with a lower value is selected as the number of stops in the objective function, and the number of stops of the vehicle is controlled within the lowest threshold, minimizing the starting frequency of stops to the greatest extent and ensuring the smooth passage of the traffic flow.

[0101] It should be noted that the first preset number threshold is less than the second preset number threshold, and the specific value can be determined according to actual needs and is not limited here.

[0102] In the above embodiment, the traffic green wave control system sets the maximum queue length as the optimization parameter of the objective function for the peak period, which can effectively alleviate the congestion during the traffic peak; while in the off-peak period, the traffic efficiency of vehicles is further improved through the optimized adjustment of the number of stops. This dynamic phased optimization method effectively adapts to the needs under different traffic flow conditions, enabling the green wave control scheme to achieve the best effect in each stage. At the same time, based on the flexible adjustment of the objective function, the signal timing scheme can be optimized more precisely, reducing the stop and delay time of vehicles, improving the traffic efficiency, and reducing fuel consumption and carbon emissions.

[0103] S211. Construct the objective function of the particle swarm optimization algorithm based on minimizing the average vehicle delay time, the number of stops, and the maximum queue length.

[0104] S212. Calculate the optimal green wave bandwidth according to the constraint conditions and the objective function to obtain the optimal timing combination scheme.

[0105] This step has been introduced in step S105 and will not be elaborated here.

[0106] S213. Determine the guiding vehicle speed between adjacent intersections according to the optimal timing combination scheme and the distance between adjacent intersections in the target section.

[0107] After obtaining the optimal signal timing combination plan for the signal lights within the target road section, the traffic green wave control system further calculates the guiding vehicle speed between adjacent intersections to provide driving references for drivers. Specifically, the system extracts the green light duration and phase difference data of adjacent intersections in the optimal signal timing combination plan. The green light duration represents the permitted time for vehicles to pass through the intersection, while the phase difference reflects the coordination relationship of signal timing between adjacent intersections. Then, the system accesses the distance matrix to obtain the distance data between adjacent intersections.

[0108] The system further uses the formula "guiding vehicle speed = distance ÷ (green light duration - phase difference)" to calculate the guiding vehicle speed between each pair of adjacent intersections. This guiding vehicle speed can ensure that vehicles pass through the road section at an appropriate speed, avoiding the situation of stopping at a red light due to arriving at the next intersection too early or too late. It should be noted that when calculating the guiding vehicle speed, usually the optimal signal timing combination plan with the least number of stops in the objective function is selected to meet the requirement of smooth and orderly vehicle passage. At the same time, since vehicles are affected by various factors such as traffic flow, vehicle types, and driving habits during actual driving, the system will reserve a certain buffer speed range (such as 10%) during calculation, so as to obtain a guiding vehicle speed range and avoid the guiding vehicle speed being too idealized.

[0109] S214. Send the guiding vehicle speed to the mobile terminals of corresponding-position drivers through the intelligent transportation guidance system for voice prompts.

[0110] Specifically, the intelligent transportation guidance system conducts real-time data docking with the traffic green wave control system to obtain real-time and dynamically updated guiding vehicle speed data. On the premise of obtaining the driver's authorization, the intelligent transportation guidance system obtains the driver's location information in real time through the positioning module. When it detects that the driver is about to enter or has entered the target road section, it pushes the guiding vehicle speed information of the corresponding road section to the driver's mobile terminal. This push is carried out through the mobile network and can adopt various methods such as instant messaging and text messages, which are not limited here.

[0111] In the above embodiment, the guiding vehicle speed between adjacent intersections within the target road section is jointly determined by the optimal signal timing combination plan and the intersection distance, and is sent to the driver's mobile terminal in real time through the intelligent transportation guidance system. This method can effectively guide drivers to pass through the green wave road section at a reasonable speed, avoiding stops or delays caused by speed mismatches. In addition, through the voice prompt function, drivers can obtain driving guidance information more intuitively, reduce decision-making delays, and improve driving safety and comfort.

[0112] The traffic green wave control system of the embodiment of the present invention is an electronic device, Figure 3 showing the schematic architecture diagram of the electronic device suitable for implementing the embodiment of the present invention.

[0113] It should be noted thatFigure 3 The electronic device shown is only an example and should not impose any restrictions on the functions and scope of use of the embodiments of the present invention.

[0114] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions (computer programs), or by controlling relevant hardware through instructions (computer programs). The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor. Among them, multiple instructions are stored in the storage medium, and the instructions can be loaded by the processor to execute any step of the method provided by the embodiments of the present invention.

[0115] Specifically, the storage medium and the processor are directly or indirectly electrically connected to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more signal lines. The computer execution instructions for implementing the data access control method are stored in the storage medium, including at least one software function module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium. The storage medium can be, but is not limited to, a random access storage medium (Random Access Memory, abbreviated as RAM), a read-only storage medium (Read Only Memory, abbreviated as ROM), a programmable read-only storage medium (Programmable Read-Only Memory, abbreviated as PROM), an erasable read-only storage medium (Erasable Programmable Read-Only Memory, abbreviated as EPROM), an electrically erasable read-only storage medium (Electric Erasable Programmable Read-Only Memory, abbreviated as EEPROM), etc. Among them, the storage medium is used to store the program, and the processor executes the program after receiving the execution instruction.

[0116] Furthermore, the software programs and modules in the above-mentioned storage medium may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components. The processor may be an integrated circuit chip having signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., which may implement or execute the various methods, steps, and logic flow diagrams disclosed in this embodiment. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0117] Since the instructions stored in the storage medium can execute the steps of any method provided in the embodiments of the present invention, the beneficial effects of any method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0118] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent transportation dynamic green wave control method, applied to a traffic green wave control system, characterized in that, The method includes: Obtaining the real-time traffic data of each lane and the emergency data announced by the traffic management department, where the real-time traffic data is collected by sensors installed at intersections; Inputting the preprocessed real-time traffic data into a neural network model trained based on historical traffic data and relevant influencing factor data, and outputting the traffic flow prediction results in different directions of each section within a preset future time; Inputting the traffic flow prediction results and the emergency data into a traffic flow theory model, and outputting the congestion levels and congestion durations in different directions of each section; Determining the adjustment priorities in different directions of target sections that meet the preset conditions according to the congestion levels and congestion durations, and obtaining the adjustment sequences in different directions of each target section; Obtaining the second distance values between each intersection; determining the sections where a continuous preset number of the second distance values are less than or equal to the preset distance threshold as the target sections that meet the preset conditions; Obtaining the first distance values between each intersection within the target section; calculating the speed average according to the historical driving speeds of the vehicle between each intersection within the target section; determining the minimum phase difference corresponding to the shortest section according to the first distance value and the speed average; determining the other phase differences of other sections according to the ratio of the first distance values of other sections in the target section to the shortest section, where the other phase differences are integer multiples of the minimum phase difference; Determining the constraint conditions of the particle swarm optimization algorithm according to the traffic flow prediction results, the phase differences between each intersection signal lights, and the preset saturation flow; Determining the peak stage according to the congestion levels and congestion durations; if the peak stage is the peak period, setting the first preset length threshold as the maximum queue length in the objective function; if the peak stage is the off-peak period, setting the first preset number threshold as the number of stops in the objective function; Constructing the objective function of the particle swarm optimization algorithm according to minimizing the average vehicle delay time, the number of stops, and the maximum queue length; Calculating the optimal green wave bandwidth according to the constraint conditions and the objective function, and obtaining the optimal timing combination scheme; Sending the optimal timing combination scheme to the traffic signal controller for real-time timing control.

2. The method according to claim 1, wherein, Before the step of constructing the objective function of the particle swarm optimization algorithm according to minimizing the average vehicle delay time, the number of stops, and the maximum queue length, it further includes: The peak stage includes the peak period and the off-peak period; The maximum queue length includes a first preset length threshold and a second preset length threshold, where the first preset length threshold is less than the second preset length threshold; The number of stops includes a first preset number threshold and a second preset number threshold, where the first preset number threshold is less than the second preset number threshold.

3. The method according to claim 1, wherein After the step of sending the optimal timing combination scheme to the traffic signal controller for real-time timing control, it further includes: Determining the guiding vehicle speed between the adjacent intersections according to the optimal timing combination scheme and the distance between the adjacent intersections within the target section; Sending the guiding vehicle speed to the mobile terminals of the corresponding position drivers through the intelligent traffic guidance system for voice prompts.

4. A traffic green wave control system, characterized in that, The traffic green wave control system includes: one or more processors and a memory; The memory is coupled to the one or more processors, and 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 traffic green wave control system to execute the method described in any one of claims 1-3.

5. A computer-readable storage medium, comprising instructions, characterized in that, When the instructions run on the traffic green wave control system, it causes the traffic green wave control system to execute the method described in any one of claims 1-3.

6. A computer program product, characterized in that, When the computer program product runs on the traffic green wave control system, it causes the traffic green wave control system to execute the method described in any one of claims 1-3.

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