Traffic flow collaborative guidance and intersection optimization method and system based on agent map

Abstract

The invention provides a traffic flow collaborative guidance and intersection optimization method and system based on an intelligent agent map. The method comprises the steps of collecting traffic element information within a preset time in a target area, constructing a regional agent map, extracting traffic operation and maintenance index data according to the regional agent map, performing processing according to the traffic operation and maintenance index data to obtain a traffic operation and maintenance conformity coefficient, and determining the traffic operation and maintenance conformity coefficient according to the traffic operation and maintenance conformity coefficient. And evaluating the traffic state of the target area according to the traffic operation and maintenance conformity coefficient, and adopting a corresponding traffic flow collaborative guidance and intersection optimization strategy, thereby realizing the traffic flow collaborative guidance and intersection optimization based on the agent map.

Description

Technical Field

[0001] This application relates to the field of intelligent traffic management technology, and more specifically, to a method and system for traffic flow cooperative guidance and intersection optimization based on intelligent agent graphs. Background Technology

[0002] Existing traffic control methods mostly rely on data from a single detector, focusing only on controlling a single element such as a vehicle or intersection. They ignore the dynamic relationship between vehicles, roads, people, and the environment, resulting in a lack of systematic control measures. Traditional technologies struggle to integrate multiple traffic elements to form a holistic understanding, and the extraction of traffic operation and maintenance indicators is one-sided, failing to accurately assess the health of regional traffic. Consequently, traffic flow guidance and intersection optimization strategies are not targeted enough and are difficult to adapt to complex and ever-changing traffic scenarios.

[0003] Therefore, how to construct a traffic cognition model with multiple interconnected elements to achieve accurate assessment and dynamic control of traffic conditions has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for traffic flow collaborative guidance and intersection optimization based on intelligent agent graphs. By collecting traffic element information within a target area over a preset time period and constructing a regional intelligent agent graph, traffic operation and maintenance indicator data is extracted from the regional intelligent agent graph. This data is then processed to obtain a traffic operation and maintenance compliance coefficient. Based on this coefficient, the traffic status of the target area is assessed, and corresponding traffic flow collaborative guidance and intersection optimization strategies are implemented to achieve traffic flow collaborative guidance and intersection optimization based on intelligent agent graphs.

[0005] This application also provides a method for traffic flow cooperative guidance and intersection optimization based on intelligent agent graphs, including the following steps: Collect traffic element information within the target area within a preset time period and construct a regional intelligent agent map; Traffic operation and maintenance indicator data are extracted based on the regional intelligent agent map; The traffic operation and maintenance index data are processed to obtain the traffic operation and maintenance compliance coefficient. The traffic status of the target area is assessed based on the traffic operation and maintenance compliance coefficient, and corresponding traffic flow coordination guidance and intersection optimization strategies are adopted.

[0006] Optionally, in the traffic flow cooperative guidance and intersection optimization method based on intelligent agent graph described in this application, the step of collecting traffic element information within a preset time period in the target area and constructing a regional intelligent agent graph includes: Collect traffic element information within the target area within a preset time period and perform standardized processing; The traffic element information includes vehicle intelligent agents, intersection intelligent agents, facility intelligent agents, environmental intelligent agents, and user intelligent agents; Entity modeling and agent relationship construction are performed on the vehicle agent, intersection agent, facility agent, environment agent, and user agent respectively to obtain a regional agent map.

[0007] Optionally, in the traffic flow cooperative guidance and intersection optimization method based on the agent graph described in this application, the step of extracting traffic operation and maintenance index data based on the regional agent graph includes: Traffic operation and maintenance index data, including traffic efficiency index data and traffic safety index data, are extracted from the regional intelligent agent map. The traffic efficiency indicators include average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate. The traffic safety indicators include the number of conflicts, the rate of violations of safe distance, and the accident rate.

[0008] Optionally, in the traffic flow cooperative guidance and intersection optimization method based on intelligent agent graph described in this application, the step of processing the traffic operation and maintenance index data to obtain the traffic operation and maintenance compliance coefficient includes: The average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate are processed through a preset traffic efficiency evaluation model to obtain a traffic efficiency compliance coefficient. The traffic safety compliance coefficient is obtained by processing the number of conflicts, the violation rate of safe distance, and the accident rate through a preset traffic safety assessment model. The traffic operation and maintenance compliance coefficient is obtained by weighting the traffic efficiency compliance coefficient and the traffic safety compliance coefficient.

[0009] Optionally, in the vehicle flow cooperative guidance and intersection optimization method based on intelligent agent graph described in this application, the step of assessing the traffic status of the target area based on the traffic operation and maintenance compliance coefficient and adopting corresponding vehicle flow cooperative guidance and intersection optimization strategies includes: The comparison results are obtained by comparing the traffic operation and maintenance compliance coefficient with the preset traffic operation and maintenance compliance threshold. If the traffic operation and maintenance compliance coefficient is greater than or equal to the preset traffic operation and maintenance compliance threshold, then the traffic status of the target area meets the requirements. If the traffic operation and maintenance compliance coefficient is less than the preset traffic operation and maintenance compliance threshold, then the traffic status of the target area does not meet the requirements, and corresponding traffic flow coordination guidance and intersection optimization strategies need to be adopted.

[0010] Optionally, the traffic flow cooperative guidance and intersection optimization method based on agent graphs described in this application further includes: The traffic flow coordination guidance and intersection optimization strategy includes a traffic flow coordination guidance strategy and an intersection optimization strategy; The traffic flow coordination and guidance strategy includes dynamic path guidance, traffic flow diversion, vehicle speed coordination, and car-following optimization. The intersection optimization strategy includes adaptive signal timing adjustment, dynamic right-of-way allocation, and spatial optimization.

[0011] Secondly, this application provides a traffic flow cooperative guidance and intersection optimization system based on intelligent agent graphs. The system includes a memory and a processor. The memory stores a program for a traffic flow cooperative guidance and intersection optimization method based on intelligent agent graphs. When the program for the traffic flow cooperative guidance and intersection optimization method based on intelligent agent graphs is executed by the processor, it implements the following steps: Collect traffic element information within the target area within a preset time period and construct a regional intelligent agent map; Traffic operation and maintenance indicator data are extracted based on the regional intelligent agent map; The traffic operation and maintenance index data are processed to obtain the traffic operation and maintenance compliance coefficient. The traffic status of the target area is assessed based on the traffic operation and maintenance compliance coefficient, and corresponding traffic flow coordination guidance and intersection optimization strategies are adopted.

[0012] Optionally, in the vehicle flow cooperative guidance and intersection optimization system based on intelligent agent graph described in this application, the step of collecting traffic element information within a preset time period in the target area and constructing a regional intelligent agent graph includes: Collect traffic element information within the target area within a preset time period and perform standardized processing; The traffic element information includes vehicle intelligent agents, intersection intelligent agents, facility intelligent agents, environmental intelligent agents, and user intelligent agents; Entity modeling and agent relationship construction are performed on the vehicle agent, intersection agent, facility agent, environment agent, and user agent respectively to obtain a regional agent map.

[0013] Optionally, in the vehicle flow cooperative guidance and intersection optimization system based on the intelligent agent graph described in this application, the step of extracting traffic operation and maintenance indicator data based on the regional intelligent agent graph includes: Traffic operation and maintenance index data, including traffic efficiency index data and traffic safety index data, are extracted from the regional intelligent agent map. The traffic efficiency indicators include average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate. The traffic safety indicators include the number of conflicts, the rate of violations of safe distance, and the accident rate.

[0014] Optionally, in the traffic flow cooperative guidance and intersection optimization system based on intelligent agent graphs described in this application, the step of processing the traffic operation and maintenance index data to obtain the traffic operation and maintenance compliance coefficient includes: The average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate are processed through a preset traffic efficiency evaluation model to obtain a traffic efficiency compliance coefficient. The traffic safety compliance coefficient is obtained by processing the number of conflicts, the violation rate of safe distance, and the accident rate through a preset traffic safety assessment model. The traffic operation and maintenance compliance coefficient is obtained by weighting the traffic efficiency compliance coefficient and the traffic safety compliance coefficient.

[0015] As can be seen from the above, the traffic flow collaborative guidance and intersection optimization method and system based on intelligent agent graph provided in this application collects traffic element information within a target area within a preset time period, constructs a regional intelligent agent graph, extracts traffic operation and maintenance index data based on the regional intelligent agent graph, processes the traffic operation and maintenance index data to obtain a traffic operation and maintenance compliance coefficient, evaluates the traffic status of the target area based on the traffic operation and maintenance compliance coefficient, and adopts corresponding traffic flow collaborative guidance and intersection optimization strategies, thereby realizing traffic flow collaborative guidance and intersection optimization based on intelligent agent graph.

[0016] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a traffic flow cooperative guidance and intersection optimization method based on intelligent agent graph provided in an embodiment of this application; Figure 2 A flowchart illustrating the construction of a regional intelligent agent graph for the traffic flow cooperative guidance and intersection optimization method based on intelligent agent graph provided in this application embodiment; Figure 3A flowchart illustrating the extraction of traffic operation and maintenance index data for the vehicle flow cooperative guidance and intersection optimization method based on intelligent agent graph provided in this application embodiment; Figure 4 The flowchart illustrates the process of obtaining the traffic operation and maintenance compliance coefficient for the vehicle flow cooperative guidance and intersection optimization method based on intelligent agent graph provided in this application embodiment. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 This is a flowchart of a traffic flow cooperative guidance and intersection optimization method based on intelligent agent graphs according to some embodiments of this application. This traffic flow cooperative guidance and intersection optimization method based on intelligent agent graphs is used in terminal devices, such as computers and mobile terminals. The traffic flow cooperative guidance and intersection optimization method based on intelligent agent graphs includes the following steps: S11. Collect traffic element information within the target area within a preset time period and construct a regional intelligent agent map; S12. Extract traffic operation and maintenance index data based on the regional intelligent agent map; S13. Process the traffic operation and maintenance index data to obtain the traffic operation and maintenance compliance coefficient; S14. Evaluate the traffic status of the target area based on the traffic operation and maintenance compliance coefficient, and adopt corresponding traffic flow coordination guidance and intersection optimization strategies.

[0022] It should be noted that existing traffic management suffers from pain points such as fragmented multi-factor operations, one-sided assessments, and outdated strategies. To address these pain points, the first step is to collect traffic element information within the target area over a preset time period, including vehicle intelligent agents, intersection intelligent agents, facility intelligent agents, environmental intelligent agents, and user intelligent agents. A regional intelligent agent graph is then constructed. Based on this graph, traffic operation and maintenance indicator data is extracted, including traffic efficiency indicators and traffic safety indicators. Traffic efficiency indicators include average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate. Traffic safety indicators include the number of conflicts, etc. The system first measures the violation rate of safe distance and the accident rate. Then, it processes the average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate through a preset traffic efficiency assessment model to obtain a traffic efficiency compliance coefficient. Next, it processes the number of conflicts, the violation rate of safe distance, and the accident rate through a preset traffic safety assessment model to obtain a traffic safety compliance coefficient. After further processing, it obtains a traffic operation and maintenance compliance coefficient. Based on the traffic operation and maintenance compliance coefficient, it assesses the traffic status of the target area and adopts corresponding traffic flow collaborative guidance and intersection optimization strategies, thereby realizing traffic flow collaborative guidance and intersection optimization based on the intelligent agent graph.

[0023] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the construction of a regional intelligent agent graph in some embodiments of the traffic flow cooperative guidance and intersection optimization method based on intelligent agent graphs in this application. According to embodiments of the present invention, the step of collecting traffic element information within a target area over a preset time period and constructing a regional intelligent agent graph includes: S21. Collect traffic element information within the target area within a preset time period and perform standardized processing; S22, The traffic element information includes vehicle intelligent agents, intersection intelligent agents, facility intelligent agents, environmental intelligent agents, and user intelligent agents; S23. Perform entity modeling and agent relationship construction processing on the vehicle agent, intersection agent, facility agent, environment agent, and user agent respectively to obtain a regional agent map.

[0024] It should be noted that, in order to accurately depict the dynamic correlation characteristics of the traffic system in the target area, firstly, full-dimensional traffic element information is collected within a preset time period (e.g., 12 hours, 5 minutes / sampling time), and standardized processing is performed. The collection scope covers five core intelligent agents: vehicles, intersections, facilities, environment, and users. After data cleaning and deduplication, spatiotemporal alignment (unified to 10-meter spatial accuracy and 5-minute temporal granularity), and format normalization (structured labels replace unstructured data), a high-quality data source is formed. Among them, vehicle intelligent agents include information such as ID, real-time location, and speed; intersection intelligent agents... The system encompasses data such as signal timing and traffic flow; the facility intelligence includes parameters such as detector status and parking space availability; the environmental intelligence integrates information such as weather and construction; and the user intelligence includes features such as travel preferences and real-time location. Based on standardized data, entity modeling is completed for each of the five types of intelligence, defining basic attributes and derived features (such as high-frequency road segments traversed by vehicles). Then, the relationships between intelligence are constructed, such as the "vehicle-intersection" traversal relationship and the "environment-vehicle" influence relationship. Relationship weights are assigned to quantify the strength of the relationship, ultimately forming a spatiotemporally coupled regional intelligence map.

[0025] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the extraction of traffic operation and maintenance indicator data in a vehicle flow collaborative guidance and intersection optimization method based on an agent graph, as described in some embodiments of this application. According to an embodiment of the present invention, the extraction of traffic operation and maintenance indicator data based on the regional agent graph includes: S31. Extract traffic operation and maintenance index data based on the regional intelligent agent map, including traffic efficiency index data and traffic safety index data; S32. The traffic efficiency index data includes average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate. S33. The traffic safety indicator data includes the number of conflicts, the rate of violation of safe distance, and the accident rate.

[0026] It should be noted that multi-dimensional traffic operation and maintenance indicator data are extracted based on the correlation data of five types of intelligent agents in the regional intelligent agent graph. The extraction process fully utilizes the spatiotemporal correlation advantages of the graph, tracing the data source through the relationship links between intelligent agents to ensure the accuracy of the indicators. Among them, the traffic efficiency indicator data focuses on the road network capacity, including: the average vehicle speed calculated by combining vehicle speed data and road segment attributes; the congestion duration statistically calculated by correlating vehicle trajectories with intersection congestion status; the average intersection delay calculated based on signal timing and queue length data from intersection intelligent agents; the traffic volume per unit time summarized from detector data and vehicle passage records; and the green light utilization rate obtained by combining signal phase duration and green light period traffic volume. The traffic safety indicator data focuses on risk prevention and control, including: the number of intersection conflicts obtained through cross-analysis of vehicle intelligent agent trajectories; the safe distance violation rate calculated based on vehicle distance monitoring data; and the accident incidence rate reported by associated user intelligent agents and recorded by facility intelligent agents. Both types of indicator data are dynamically linked with the intelligent agent graph to ensure real-time reflection of the regional traffic operation and maintenance status.

[0027] Please refer to Figure 4 , Figure 4 This is a flowchart illustrating the process of obtaining a traffic operation and maintenance compliance coefficient using a vehicle flow cooperative guidance and intersection optimization method based on an agent graph, as described in some embodiments of this application. According to an embodiment of the present invention, processing the traffic operation and maintenance indicator data to obtain the traffic operation and maintenance compliance coefficient includes: S41. The average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate are processed through a preset traffic efficiency evaluation model to obtain the traffic efficiency compliance coefficient. S42. Based on the number of conflicts, the violation rate of safe distance, and the accident rate, the traffic safety compliance coefficient is obtained by processing them through a preset traffic safety assessment model. S43. The traffic efficiency compliance coefficient and the traffic safety compliance coefficient are weighted and processed to obtain the traffic operation and maintenance compliance coefficient.

[0028] It should be noted that, in order to quantitatively assess the traffic operation and maintenance status of the target area, the compliance coefficients of efficiency and safety dimensions are generated step by step, and then a comprehensive result is obtained through weighted fusion. First, five efficiency indicators, such as average vehicle speed and congestion duration, are input into a preset traffic efficiency assessment model. The model calculates the score based on the weight of each indicator (e.g., average vehicle speed accounts for 30%, and traffic volume per unit time accounts for 25%), combined with the deviation between historical best data and the current value, and finally outputs a traffic efficiency compliance coefficient in the range of 0-1. The closer the coefficient is to 1, the better the efficiency. Simultaneously, three safety indicators, such as the number of conflicts and the violation rate of safe distance, are input into a preset traffic safety assessment model. The model calculates the traffic safety compliance coefficient through risk level mapping (e.g., an accident rate ≥ 0.5 times / hour corresponds to a low score), which is also represented by 0-1 to indicate the degree of safety compliance. Finally, the two types of coefficients are weighted according to different weights (which can be adjusted according to regional characteristics). For example, the traffic operation and maintenance compliance coefficient = efficiency coefficient × 0.6 + safety coefficient × 0.4, forming a comprehensive assessment value that takes into account both traffic efficiency and operational safety.

[0029] According to an embodiment of the present invention, the step of assessing the traffic status of the target area based on the traffic operation and maintenance compliance coefficient and adopting corresponding traffic flow coordination guidance and intersection optimization strategies includes: The comparison results are obtained by comparing the traffic operation and maintenance compliance coefficient with the preset traffic operation and maintenance compliance threshold. If the traffic operation and maintenance compliance coefficient is greater than or equal to the preset traffic operation and maintenance compliance threshold, then the traffic status of the target area meets the requirements. If the traffic operation and maintenance compliance coefficient is less than the preset traffic operation and maintenance compliance threshold, then the traffic status of the target area does not meet the requirements, and corresponding traffic flow coordination guidance and intersection optimization strategies need to be adopted.

[0030] It should be noted that after the traffic operation and maintenance compliance coefficient is generated, it needs to be accurately compared with the preset threshold to scientifically determine whether the traffic status of the target area meets the standards. The preset traffic operation and maintenance compliance threshold is not a fixed value, but is formulated in combination with the regional road network level (such as main roads / local roads), time characteristics (such as peak / off-peak), and historical operation and maintenance data. For example, the threshold for main roads during the morning peak is set to 0.7, and the threshold for local roads during the off-peak is set to 0.6 to ensure that the assessment fits the actual scenario. If the comparison results show that the traffic operation and maintenance compliance coefficient is ≥ the preset threshold, it means that the current average vehicle speed, accident rate and other core indicators are within the safe and efficient range, the traffic status of the target area meets the operation and maintenance requirements, and the system can maintain the normal monitoring mode. If the coefficient is < the preset threshold, it indicates that there are shortcomings in traffic efficiency or safety, and the control mechanism needs to be activated immediately.

[0031] According to an embodiment of the present invention, it further includes: The traffic flow coordination guidance and intersection optimization strategy includes a traffic flow coordination guidance strategy and an intersection optimization strategy; The traffic flow coordination and guidance strategy includes dynamic path guidance, traffic flow diversion, vehicle speed coordination, and car-following optimization. The intersection optimization strategy includes adaptive signal timing adjustment, dynamic right-of-way allocation, and spatial optimization.

[0032] It should be noted that for target areas with substandard traffic conditions, a combined strategy including traffic flow coordination and intersection optimization is implemented. The two strategies rely on real-time data linkage based on the regional intelligent agent map. The traffic flow coordination strategy focuses on the balance of traffic flow across the entire road network, including: dynamic route guidance (combining vehicle and intersection intelligent agent data to push congestion avoidance routes to connected vehicles), traffic diversion (guiding traffic flow to less congested side roads through guidance screens), vehicle speed coordination (pushing green wave suggested speeds to vehicles via V2X), and car-following optimization (guiding vehicles in the same lane to maintain a safe distance of 2-3 seconds to reduce sudden braking fluctuations). The intersection optimization strategy addresses bottlenecks at nodes, including: adaptive signal timing adjustment (dynamically increasing or decreasing the duration of each phase based on the real-time traffic flow of the intersection intelligent agent), dynamic right-of-way allocation (temporarily converting straight lanes into left-turn lanes during peak hours and prioritizing pedestrian right-of-way during off-peak hours), and space optimization (activating tidal lanes or setting up drop-off and pick-up zones to improve the utilization rate of intersection space). The two strategies work together to quickly improve traffic conditions.

[0033] Secondly, the present invention also discloses a traffic flow cooperative guidance and intersection optimization system based on intelligent agent graphs, including a memory and a processor. The memory includes a method program for traffic flow cooperative guidance and intersection optimization based on intelligent agent graphs. When the method program for traffic flow cooperative guidance and intersection optimization based on intelligent agent graphs is executed by the processor, it implements the following steps: Collect traffic element information within the target area within a preset time period and construct a regional intelligent agent map; Traffic operation and maintenance indicator data are extracted based on the regional intelligent agent map; The traffic operation and maintenance index data are processed to obtain the traffic operation and maintenance compliance coefficient. The traffic status of the target area is assessed based on the traffic operation and maintenance compliance coefficient, and corresponding traffic flow coordination guidance and intersection optimization strategies are adopted.

[0034] It should be noted that existing traffic management suffers from pain points such as fragmented multi-factor operations, one-sided assessments, and outdated strategies. To address these pain points, the first step is to collect traffic element information within the target area over a preset time period, including vehicle intelligent agents, intersection intelligent agents, facility intelligent agents, environmental intelligent agents, and user intelligent agents. A regional intelligent agent graph is then constructed. Based on this graph, traffic operation and maintenance indicator data is extracted, including traffic efficiency indicators and traffic safety indicators. Traffic efficiency indicators include average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate. Traffic safety indicators include the number of conflicts, etc. The system first measures the violation rate of safe distance and the accident rate. Then, it processes the average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate through a preset traffic efficiency assessment model to obtain a traffic efficiency compliance coefficient. Next, it processes the number of conflicts, the violation rate of safe distance, and the accident rate through a preset traffic safety assessment model to obtain a traffic safety compliance coefficient. After further processing, it obtains a traffic operation and maintenance compliance coefficient. Based on the traffic operation and maintenance compliance coefficient, it assesses the traffic status of the target area and adopts corresponding traffic flow collaborative guidance and intersection optimization strategies, thereby realizing traffic flow collaborative guidance and intersection optimization based on the intelligent agent graph.

[0035] According to an embodiment of the present invention, the step of collecting traffic element information within a target area within a preset time period and constructing a regional intelligent agent map includes: Collect traffic element information within the target area within a preset time period and perform standardized processing; The traffic element information includes vehicle intelligent agents, intersection intelligent agents, facility intelligent agents, environmental intelligent agents, and user intelligent agents; Entity modeling and agent relationship construction are performed on the vehicle agent, intersection agent, facility agent, environment agent, and user agent respectively to obtain a regional agent map.

[0036] It should be noted that, in order to accurately depict the dynamic correlation characteristics of the traffic system in the target area, firstly, full-dimensional traffic element information is collected within a preset time period (e.g., 12 hours, 5 minutes / sampling time), and standardized processing is performed. The collection scope covers five core intelligent agents: vehicles, intersections, facilities, environment, and users. After data cleaning and deduplication, spatiotemporal alignment (unified to 10-meter spatial accuracy and 5-minute temporal granularity), and format normalization (structured labels replace unstructured data), a high-quality data source is formed. Among them, vehicle intelligent agents include information such as ID, real-time location, and speed; intersection intelligent agents... The system encompasses data such as signal timing and traffic flow; the facility intelligence includes parameters such as detector status and parking space availability; the environmental intelligence integrates information such as weather and construction; and the user intelligence includes features such as travel preferences and real-time location. Based on standardized data, entity modeling is completed for each of the five types of intelligence, defining basic attributes and derived features (such as high-frequency road segments traversed by vehicles). Then, the relationships between intelligence are constructed, such as the "vehicle-intersection" traversal relationship and the "environment-vehicle" influence relationship. Relationship weights are assigned to quantify the strength of the relationship, ultimately forming a spatiotemporally coupled regional intelligence map.

[0037] According to an embodiment of the present invention, the step of extracting traffic operation and maintenance indicator data based on the regional intelligent agent map includes: Traffic operation and maintenance index data, including traffic efficiency index data and traffic safety index data, are extracted from the regional intelligent agent map. The traffic efficiency indicators include average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate. The traffic safety indicators include the number of conflicts, the rate of violations of safe distance, and the accident rate.

[0038] It should be noted that multi-dimensional traffic operation and maintenance indicator data are extracted based on the correlation data of five types of intelligent agents in the regional intelligent agent graph. The extraction process fully utilizes the spatiotemporal correlation advantages of the graph, tracing the data source through the relationship links between intelligent agents to ensure the accuracy of the indicators. Among them, the traffic efficiency indicator data focuses on the road network capacity, including: the average vehicle speed calculated by combining vehicle speed data and road segment attributes; the congestion duration statistically calculated by correlating vehicle trajectories with intersection congestion status; the average intersection delay calculated based on signal timing and queue length data from intersection intelligent agents; the traffic volume per unit time summarized from detector data and vehicle passage records; and the green light utilization rate obtained by combining signal phase duration and green light period traffic volume. The traffic safety indicator data focuses on risk prevention and control, including: the number of intersection conflicts obtained through cross-analysis of vehicle intelligent agent trajectories; the safe distance violation rate calculated based on vehicle distance monitoring data; and the accident incidence rate reported by associated user intelligent agents and recorded by facility intelligent agents. Both types of indicator data are dynamically linked with the intelligent agent graph to ensure real-time reflection of the regional traffic operation and maintenance status.

[0039] According to an embodiment of the present invention, the step of processing the traffic operation and maintenance index data to obtain the traffic operation and maintenance compliance coefficient includes: The average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate are processed through a preset traffic efficiency evaluation model to obtain a traffic efficiency compliance coefficient. The traffic safety compliance coefficient is obtained by processing the number of conflicts, the violation rate of safe distance, and the accident rate through a preset traffic safety assessment model. The traffic operation and maintenance compliance coefficient is obtained by weighting the traffic efficiency compliance coefficient and the traffic safety compliance coefficient.

[0040] It should be noted that, in order to quantitatively assess the traffic operation and maintenance status of the target area, the compliance coefficients of efficiency and safety dimensions are generated step by step, and then a comprehensive result is obtained through weighted fusion. First, five efficiency indicators, such as average vehicle speed and congestion duration, are input into a preset traffic efficiency assessment model. The model calculates the score based on the weight of each indicator (e.g., average vehicle speed accounts for 30%, and traffic volume per unit time accounts for 25%), combined with the deviation between historical best data and the current value, and finally outputs a traffic efficiency compliance coefficient in the range of 0-1. The closer the coefficient is to 1, the better the efficiency. Simultaneously, three safety indicators, such as the number of conflicts and the violation rate of safe distance, are input into a preset traffic safety assessment model. The model calculates the traffic safety compliance coefficient through risk level mapping (e.g., an accident rate ≥ 0.5 times / hour corresponds to a low score), which is also represented by 0-1 to indicate the degree of safety compliance. Finally, the two types of coefficients are weighted according to different weights (which can be adjusted according to regional characteristics). For example, the traffic operation and maintenance compliance coefficient = efficiency coefficient × 0.6 + safety coefficient × 0.4, forming a comprehensive assessment value that takes into account both traffic efficiency and operational safety.

[0041] According to an embodiment of the present invention, the step of assessing the traffic status of the target area based on the traffic operation and maintenance compliance coefficient and adopting corresponding traffic flow coordination guidance and intersection optimization strategies includes: The comparison results are obtained by comparing the traffic operation and maintenance compliance coefficient with the preset traffic operation and maintenance compliance threshold. If the traffic operation and maintenance compliance coefficient is greater than or equal to the preset traffic operation and maintenance compliance threshold, then the traffic status of the target area meets the requirements. If the traffic operation and maintenance compliance coefficient is less than the preset traffic operation and maintenance compliance threshold, then the traffic status of the target area does not meet the requirements, and corresponding traffic flow coordination guidance and intersection optimization strategies need to be adopted.

[0042] It should be noted that after the traffic operation and maintenance compliance coefficient is generated, it needs to be accurately compared with the preset threshold to scientifically determine whether the traffic status of the target area meets the standards. The preset traffic operation and maintenance compliance threshold is not a fixed value, but is formulated in combination with the regional road network level (such as main roads / local roads), time characteristics (such as peak / off-peak), and historical operation and maintenance data. For example, the threshold for main roads during the morning peak is set to 0.7, and the threshold for local roads during the off-peak is set to 0.6 to ensure that the assessment fits the actual scenario. If the comparison results show that the traffic operation and maintenance compliance coefficient is ≥ the preset threshold, it means that the current average vehicle speed, accident rate and other core indicators are within the safe and efficient range, the traffic status of the target area meets the operation and maintenance requirements, and the system can maintain the normal monitoring mode. If the coefficient is < the preset threshold, it indicates that there are shortcomings in traffic efficiency or safety, and the control mechanism needs to be activated immediately.

[0043] According to an embodiment of the present invention, it further includes: The traffic flow coordination guidance and intersection optimization strategy includes a traffic flow coordination guidance strategy and an intersection optimization strategy; The traffic flow coordination and guidance strategy includes dynamic path guidance, traffic flow diversion, vehicle speed coordination, and car-following optimization. The intersection optimization strategy includes adaptive signal timing adjustment, dynamic right-of-way allocation, and spatial optimization.

[0044] It should be noted that for target areas with substandard traffic conditions, a combined strategy including traffic flow coordination and intersection optimization is implemented. The two strategies rely on real-time data linkage based on the regional intelligent agent map. The traffic flow coordination strategy focuses on the balance of traffic flow across the entire road network, including: dynamic route guidance (combining vehicle and intersection intelligent agent data to push congestion avoidance routes to connected vehicles), traffic diversion (guiding traffic flow to less congested side roads through guidance screens), vehicle speed coordination (pushing green wave suggested speeds to vehicles via V2X), and car-following optimization (guiding vehicles in the same lane to maintain a safe distance of 2-3 seconds to reduce sudden braking fluctuations). The intersection optimization strategy addresses bottlenecks at nodes, including: adaptive signal timing adjustment (dynamically increasing or decreasing the duration of each phase based on the real-time traffic flow of the intersection intelligent agent), dynamic right-of-way allocation (temporarily converting straight lanes into left-turn lanes during peak hours and prioritizing pedestrian right-of-way during off-peak hours), and space optimization (activating tidal lanes or setting up drop-off and pick-up zones to improve the utilization rate of intersection space). The two strategies work together to quickly improve traffic conditions.

[0045] The present invention discloses a method and system for coordinated traffic flow guidance and intersection optimization based on intelligent agent graphs. This method collects traffic element information within a target area over a preset time period and constructs a regional intelligent agent graph. Traffic operation and maintenance indicator data is extracted from the regional intelligent agent graph, processed to obtain a traffic operation and maintenance compliance coefficient, and the traffic status of the target area is evaluated based on the compliance coefficient. Corresponding coordinated traffic flow guidance and intersection optimization strategies are then implemented, thereby achieving coordinated traffic flow guidance and intersection optimization based on intelligent agent graphs.

[0046] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0047] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0048] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0049] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0050] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for traffic flow cooperative guidance and intersection optimization based on intelligent agent graph, characterized in that, Includes the following steps: Collect traffic element information within the target area within a preset time period and construct a regional intelligent agent map; Traffic operation and maintenance indicator data are extracted based on the regional intelligent agent map; The traffic operation and maintenance index data are processed to obtain the traffic operation and maintenance compliance coefficient. The traffic status of the target area is assessed based on the traffic operation and maintenance compliance coefficient, and corresponding traffic flow coordination guidance and intersection optimization strategies are adopted.

2. The method for traffic flow cooperative guidance and intersection optimization based on intelligent agent graph as described in claim 1, characterized in that, The process of collecting traffic element information within a preset time period within the target area and constructing a regional intelligent agent map includes: Collect traffic element information within the target area within a preset time period and perform standardized processing; The traffic element information includes vehicle intelligent agents, intersection intelligent agents, facility intelligent agents, environmental intelligent agents, and user intelligent agents; Entity modeling and agent relationship construction are performed on the vehicle agent, intersection agent, facility agent, environment agent, and user agent respectively to obtain a regional agent map.

3. The method for traffic flow cooperative guidance and intersection optimization based on intelligent agent graph as described in claim 2, characterized in that, The step of extracting traffic operation and maintenance indicator data based on the regional intelligent agent map includes: Traffic operation and maintenance index data, including traffic efficiency index data and traffic safety index data, are extracted from the regional intelligent agent map. The traffic efficiency indicators include average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate. The traffic safety indicators include the number of conflicts, the rate of violations of safe distance, and the accident rate.

4. The traffic flow cooperative guidance and intersection optimization method based on intelligent agent graph as described in claim 3, characterized in that, The process of processing the traffic operation and maintenance indicator data to obtain the traffic operation and maintenance compliance coefficient includes: The average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate are processed through a preset traffic efficiency evaluation model to obtain a traffic efficiency compliance coefficient. The traffic safety compliance coefficient is obtained by processing the number of conflicts, the violation rate of safe distance, and the accident rate through a preset traffic safety assessment model. The traffic operation and maintenance compliance coefficient is obtained by weighting the traffic efficiency compliance coefficient and the traffic safety compliance coefficient.

5. The method for traffic flow cooperative guidance and intersection optimization based on intelligent agent graph as described in claim 1, characterized in that, The process of assessing the traffic status of the target area based on the traffic operation and maintenance compliance coefficient, and adopting corresponding traffic flow coordination guidance and intersection optimization strategies, includes: The comparison results are obtained by comparing the traffic operation and maintenance compliance coefficient with the preset traffic operation and maintenance compliance threshold. If the traffic operation and maintenance compliance coefficient is greater than or equal to the preset traffic operation and maintenance compliance threshold, then the traffic status of the target area meets the requirements. If the traffic operation and maintenance compliance coefficient is less than the preset traffic operation and maintenance compliance threshold, then the traffic status of the target area does not meet the requirements, and corresponding traffic flow coordination guidance and intersection optimization strategies need to be adopted.

6. The method for traffic flow cooperative guidance and intersection optimization based on intelligent agent graph as described in claim 5, characterized in that, Also includes: The traffic flow coordination guidance and intersection optimization strategy includes a traffic flow coordination guidance strategy and an intersection optimization strategy; The traffic flow coordination and guidance strategy includes dynamic path guidance, traffic flow diversion, vehicle speed coordination, and car-following optimization. The intersection optimization strategy includes adaptive signal timing adjustment, dynamic right-of-way allocation, and spatial optimization.

7. A traffic flow cooperative guidance and intersection optimization system based on intelligent agent graph, characterized in that, The system includes a memory and a processor. The memory contains a program for a traffic flow cooperative guidance and intersection optimization method based on an agent graph. When the program for the traffic flow cooperative guidance and intersection optimization method based on an agent graph is executed by the processor, it performs the following steps: Collect traffic element information within the target area within a preset time period and construct a regional intelligent agent map; Traffic operation and maintenance indicator data are extracted based on the regional intelligent agent map; The traffic operation and maintenance index data are processed to obtain the traffic operation and maintenance compliance coefficient. The traffic status of the target area is assessed based on the traffic operation and maintenance compliance coefficient, and corresponding traffic flow coordination guidance and intersection optimization strategies are adopted.

8. The traffic flow cooperative guidance and intersection optimization system based on intelligent agent graph as described in claim 7, characterized in that, The process of collecting traffic element information within a preset time period within the target area and constructing a regional intelligent agent map includes: Collect traffic element information within the target area within a preset time period and perform standardized processing; The traffic element information includes vehicle intelligent agents, intersection intelligent agents, facility intelligent agents, environmental intelligent agents, and user intelligent agents; Entity modeling and agent relationship construction are performed on the vehicle agent, intersection agent, facility agent, environment agent, and user agent respectively to obtain a regional agent map.

9. The traffic flow cooperative guidance and intersection optimization system based on intelligent agent graph as described in claim 8, characterized in that, The step of extracting traffic operation and maintenance indicator data based on the regional intelligent agent map includes: Traffic operation and maintenance index data, including traffic efficiency index data and traffic safety index data, are extracted from the regional intelligent agent map. The traffic efficiency indicators include average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate. The traffic safety indicators include the number of conflicts, the rate of violations of safe distance, and the accident rate.

10. The traffic flow cooperative guidance and intersection optimization system based on intelligent agent graph as described in claim 9, characterized in that, The process of processing the traffic operation and maintenance indicator data to obtain the traffic operation and maintenance compliance coefficient includes: The average vehicle speed, congestion duration, average intersection delay, traffic volume per unit time, and green light utilization rate are processed through a preset traffic efficiency evaluation model to obtain a traffic efficiency compliance coefficient. The traffic safety compliance coefficient is obtained by processing the number of conflicts, the violation rate of safe distance, and the accident rate through a preset traffic safety assessment model. The traffic operation and maintenance compliance coefficient is obtained by weighting the traffic efficiency compliance coefficient and the traffic safety compliance coefficient.

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