A sub-region division method and device of an urban road network, an electronic device, and a medium

Abstract

The application provides a city road network sub-region division method and device, electronic equipment and medium, comprising: acquiring first static traffic data and first dynamic traffic flow data of each intersection in a to-be-divided city road network in a vehicle-road cooperation system; inputting the first static traffic data and the first dynamic traffic flow data of any two intersections into an intersection correlation mathematical model to determine a first correlation value between the two intersections; and performing sub-region division on the to-be-divided city road network based on a plurality of first correlation values. The static traffic data and the dynamic traffic flow data of the intersection are collected in real time in the vehicle-road cooperation system, the intersection correlation mathematical model obtains the correlation value between the intersections according to the static traffic data and the dynamic traffic flow data, and the road network sub-region is dynamically divided according to the correlation value, thereby improving the accuracy of the road network sub-region division.

Description

Technical Field

[0001] This application relates to the field of urban traffic network division technology, and in particular to a method, apparatus, electronic device and medium for dividing urban road networks into sub-regions. Background Technology

[0002] While significant progress has been made in the research of traffic signal optimization control for road networks, the large scale of urban road networks and the numerous intersections, coupled with the mutual influence and dynamic fluctuations in traffic flow between adjacent intersections during the coordinated operation of regional road networks with multiple intersections, make traffic signal control more complex and challenging. Due to the sheer complexity and vastness of road networks, general research on traffic signal optimization control is insufficient for direct overall optimization of the entire network. Existing research has extensively studied road network traffic signal control, and by dividing the road network into several reasonable sub-regions and coordinating the signal lights at intersections within each sub-region, the overall signal control effect of the road network can be guaranteed. Therefore, how to dynamically divide the road network and improve the accuracy of sub-region division has become a crucial technical challenge. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method, device, electronic device, and medium for sub-region division of urban road networks. By collecting static traffic data and dynamic traffic flow data at intersections in real time within a vehicle-road cooperative system, a mathematical model of the correlation degree between intersections is used to obtain the correlation degree value between intersections based on the static and dynamic traffic flow data. This correlation degree value is then used to dynamically divide the road network into sub-regions, improving the accuracy of sub-region division. This enhances the coordination between vehicles and infrastructure within the vehicle-road cooperative system, thereby improving road traffic safety.

[0004] This application provides a method for sub-region division of an urban road network, the method comprising:

[0005] In the vehicle-road cooperative system, acquire the first static traffic data and the first dynamic traffic flow data of each intersection in the urban road network to be divided;

[0006] Input the first static traffic data and the first dynamic traffic flow data of any two intersections into the mathematical model of the correlation degree between intersections to determine the first correlation degree value between the two intersections.

[0007] Based on multiple first correlation values, the urban road network to be divided into sub-regions.

[0008] In one possible implementation, after dividing the urban road network to be divided into sub-regions based on multiple first correlation values, the sub-region division method further includes:

[0009] Obtain the mean of the second static traffic data and the mean of the second dynamic traffic flow data for each intersection in the urban road network to be divided within a preset time period at the current time.

[0010] The mathematical model for the correlation degree between intersections determines the second correlation degree value between every two intersections based on the mean of the second static traffic data and the mean of the second dynamic traffic flow data for each intersection.

[0011] The sub-regions of the urban road network to be divided are updated based on multiple second correlation values.

[0012] In one possible implementation, for the first intersection and the second intersection, the step of inputting the first static traffic data and the first dynamic traffic flow data of any two intersections into the mathematical model of the correlation degree between the intersections to determine the first correlation degree value between the two intersections includes:

[0013] The mathematical model for the correlation degree between intersections determines the first segment correlation degree value of the first intersection based on the first static traffic data and the first dynamic traffic flow data of the first intersection.

[0014] The mathematical model for the correlation degree between intersections determines the correlation degree value of the second road segment of the second intersection based on the second static traffic data and the second dynamic traffic flow data of the second intersection.

[0015] The mathematical model for the correlation degree between intersections determines the first correlation degree value between the first intersection and the second intersection based on the correlation degree value of the first road segment and the correlation degree value of the second road segment.

[0016] In one possible implementation, the step of dividing the urban road network to be divided into sub-regions based on multiple first correlation values ​​includes:

[0017] The reciprocal of the first correlation value between any two intersections is determined as the connectivity weight between any two intersections in the urban road network to be divided.

[0018] For the first intersection, the connecting edge corresponding to the maximum connectivity weight of the first intersection is removed in the urban road network to be divided, and it is detected whether the number of removals reaches a preset threshold.

[0019] If not, continue removing the connecting edge corresponding to the maximum connecting edge weight from the remaining connecting edge weights of the first intersection until the number of removals reaches the preset threshold, at which point the removal of connecting edges of the first intersection is stopped, and the intersection currently connected to the first intersection and the first intersection are assigned to the same sub-region.

[0020] In one possible implementation, the first dynamic traffic flow data includes:

[0021] The average time for vehicles to pass through the road segment corresponding to the intersection, the traffic flow cycle of the road segment corresponding to the intersection, the vehicle type of the road segment corresponding to the intersection, the weather of the road segment corresponding to the intersection, the traffic flow from the upstream intersection to the intersection, the total traffic flow to the intersection within a preset time period, and the average speed of the road segment at the intersection.

[0022] In one possible implementation, the traffic flow cycle is determined by the following steps:

[0023] The first historical average traffic flow on weekdays and the second historical average traffic flow on non-weekdays were determined for the road segment.

[0024] If the current time is a weekday, the traffic flow cycle is determined based on the ratio of the real-time traffic flow of vehicles on the road segment to the first historical average traffic flow.

[0025] If the current time is a non-working day, the traffic flow cycle is determined based on the ratio of the real-time traffic flow of vehicles on the road segment to the second historical average traffic flow.

[0026] In one possible implementation, the vehicle type is determined by the following steps:

[0027] Obtain the total number of various vehicle types passing through the road segment;

[0028] Based on the total number of various vehicle types and the number of medium and large vehicles, the proportion of medium and large vehicles in each road segment is determined.

[0029] The vehicle type is determined based on the proportion of medium and large-sized vehicles and the preset relationship table between vehicle type and influence value.

[0030] This application embodiment also provides a sub-region division device for urban road networks, the sub-region division device comprising:

[0031] The acquisition module is used to acquire the first static traffic data and the first dynamic traffic flow data of each intersection in the urban road network to be divided in the vehicle-road cooperative system.

[0032] The determination module is used to input the first static traffic data and the first dynamic traffic flow data of any two intersections into the mathematical model of the correlation degree between intersections, and determine the first correlation degree value between the two intersections.

[0033] The partitioning module is used to divide the urban road network to be divided into sub-regions based on multiple first correlation values.

[0034] This application also provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the sub-region division method of the urban road network described above are performed.

[0035] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the urban road network sub-region division method described above.

[0036] This application provides a method, apparatus, electronic device, and medium for sub-region division of an urban road network. The sub-region division method includes: acquiring first static traffic data and first dynamic traffic flow data for each intersection in the urban road network to be divided in a vehicle-road cooperative system; inputting the first static traffic data and first dynamic traffic flow data of any two intersections into an intersection correlation mathematical model to determine a first correlation value between the two intersections; and dividing the urban road network into sub-regions based on multiple first correlation values. By collecting static traffic data and dynamic traffic flow data of intersections in real time in the vehicle-road cooperative system, the intersection correlation mathematical model obtains the correlation value between intersections based on the static traffic data and dynamic traffic flow data, and dynamically divides the road network into sub-regions based on the correlation value, thereby improving the accuracy of road network sub-region division. This improves the coordination between vehicles and infrastructure in the vehicle-road cooperative system, thereby enhancing road traffic safety.

[0037] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments 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.

[0039] Figure 1 One of the flowcharts for a method of sub-region division of an urban road network provided in an embodiment of this application;

[0040] Figure 2 A second flowchart illustrating a method for sub-region division of an urban road network provided in an embodiment of this application;

[0041] Figure 3 One of the structural schematic diagrams of a sub-region division device for an urban road network provided in an embodiment of this application;

[0042] Figure 4 A second schematic diagram of a sub-region division device for an urban road network provided in an embodiment of this application;

[0043] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0045] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically 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 to illustrate selected embodiments of the 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.

[0046] In order to enable those skilled in the art to use the content of this application and combine it with the specific application scenario of "dividing urban road networks into sub-regions", the following implementation method is given. For those skilled in the art, the general principles defined herein can be applied to other embodiments and application scenarios without departing from the spirit and scope of this application.

[0047] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of urban traffic network delineation technology.

[0048] While significant progress has been made in the research of traffic signal optimization control for road networks, the large scale of urban road networks and the numerous intersections, coupled with the mutual influence and dynamic fluctuations in traffic flow between adjacent intersections during the coordinated operation of regional road networks with multiple intersections, make traffic signal control more complex and challenging. Due to the sheer complexity and vastness of road networks, general research on traffic signal optimization control is insufficient for direct overall optimization of the entire network. Existing research has extensively studied road network traffic signal control, and by dividing the road network into several reasonable sub-regions and coordinating the signal lights at intersections within each sub-region, the overall signal control effect of the road network can be guaranteed. Therefore, how to dynamically divide the road network and improve the accuracy of sub-region division has become a crucial technical challenge.

[0049] Based on this, this application provides a method for sub-region division of urban road networks. By collecting static traffic data and dynamic traffic flow data at intersections in real time within a vehicle-road cooperative system, a mathematical model of the correlation degree between intersections is used to obtain correlation values ​​between intersections based on the static and dynamic traffic flow data. This correlation value is then used to dynamically divide the road network into sub-regions, improving the accuracy of sub-region division. This enhances the coordination between vehicles and infrastructure within the vehicle-road cooperative system, thereby improving road traffic safety.

[0050] Please see Figure 1 , Figure 1 This is one of the flowcharts for a method of sub-region division of an urban road network provided in an embodiment of this application. For example... Figure 1 As shown in the embodiments of this application, the sub-region division method includes:

[0051] S101: Obtain the first static traffic data and the first dynamic traffic flow data of each intersection in the urban road network to be divided in the vehicle-road cooperative system.

[0052] In this step, the first static traffic data and the first dynamic traffic flow data of each intersection in the urban road network to be divided are obtained in the vehicle-road cooperative system.

[0053] Here, the data source may not be a vehicle-to-infrastructure (V2I) system, but may include sensors such as surveillance cameras and speed measurement radar. These sensors can provide traffic data, but the collection of these devices does not necessarily constitute a V2I system. Using the data provided by these sensors to directly perform mathematical modeling of the inter-intersection correlation to achieve sub-zone division can be an alternative solution for dynamic sub-zone division.

[0054] The first dynamic traffic flow data includes: the average time to pass through the road segment corresponding to the intersection, the traffic flow cycle of the road segment corresponding to the intersection, the vehicle type of the road segment corresponding to the intersection, the weather of the road segment corresponding to the intersection, the traffic flow from the upstream intersection to the intersection, the total traffic flow to the intersection within a preset time period, and the average speed of the road segment at the intersection.

[0055] The first static traffic data includes static factors such as the number of lanes on the road segment corresponding to the intersection, the distance between intersections, and the number of intersection branches.

[0056] Compared to traditional solutions, this invention adds four parameters: vehicle type, weather, traffic flow cycle, and average vehicle speed.

[0057] ① The vehicle type factor refers to the distribution of vehicle types in the traffic flow. When large trucks account for a large proportion of the traffic flow, the travel time between two intersections will be longer, the traffic volume will be reduced, and the distance between the front and rear of vehicles will be increased in order to ensure safe driving. Therefore, the correlation between the two intersections will be weakened, thus increasing the vehicle type factor.

[0058] ② The impact of weather conditions on the inter-intersection connectivity is similar to that of the vehicle type factor. In traditional schemes, weather affects traffic flow and travel time, thus indirectly affecting the inter-intersection connectivity. This scheme adds a weather factor, using vehicle speed measurement radar in the vehicle-road cooperative system to obtain the average vehicle speed, and using the ratio of the average speed to the road segment's design speed as the impact of weather conditions on the inter-intersection connectivity.

[0059] ③ The traffic flow cycle factor refers to the impact of time on the correlation between intersections. The distribution of traffic flow in the road network differs between weekdays and weekends. The traffic flow cycle factor is obtained by calculating the ratio of the current traffic flow to the historical average traffic flow. A larger traffic flow cycle factor indicates a higher traffic flow in the current road segment, and a higher traffic flow indicates a stronger correlation between the two intersections. Different values ​​for the traffic flow cycle factor on weekdays and weekends more closely reflect the distribution of traffic flow at different times.

[0060] ④ The average vehicle speed is the average speed of vehicles on the current road segment. Traditional solutions indirectly affect the connectivity between intersections through travel time. However, in reality, the factors affecting travel time on different road segments include not only the segment length but also the segment's design speed. Therefore, this invention adds an average vehicle speed factor, which is the ratio of the average speed of the road segment to the segment's design speed. This directly reflects the impact of the current speed and the segment's design speed on the connectivity of intersections. This is more reasonable than the traditional solution that indirectly affects connectivity through travel time.

[0061] In one possible implementation, the traffic flow cycle is determined by the following steps:

[0062] A: Determine the first historical average traffic flow on weekdays and the second historical average traffic flow on non-weekdays for the road segment.

[0063] Here, the first historical average traffic flow on weekdays and the second historical average traffic flow on non-weekdays are determined for the road segment.

[0064] B: If the current time is a working day, the traffic flow cycle is determined based on the ratio of the real-time traffic flow of vehicles on the road segment to the first historical average traffic flow; if the current time is a non-working day, the traffic flow cycle is determined based on the ratio of the real-time traffic flow of vehicles on the road segment to the second historical average traffic flow.

[0065] Here, the traffic flow cycle is obtained by comparing the current traffic flow of vehicles on the current road segment with the historical traffic flow. The traffic flow cycle is determined by calculating the ratio of the current real-time traffic flow to the historical average traffic flow by statistically analyzing the average traffic flow of vehicles on weekdays and non-weekdays. If the day is a weekday, the traffic flow cycle is determined by the ratio of the real-time traffic flow of vehicles on the road segment to the first historical average traffic flow. If the day is a non-weekday, the traffic flow cycle is determined by the ratio of the real-time traffic flow of vehicles on the road segment to the second historical average traffic flow.

[0066] In one possible implementation, the vehicle type is determined by the following steps:

[0067] a: Get the total number of vehicles of various types passing through the road segment.

[0068] Here, the vehicle-road cooperative system can obtain the total number of various vehicle types in the current road segment in real time.

[0069] b: Based on the total number of various vehicle types and the number of medium and large vehicles, determine the proportion of medium and large vehicles in each road segment.

[0070] Here, the proportion of medium and large vehicles in a road segment is determined based on the total number of various vehicle types and the number of medium and large vehicles.

[0071] c: Based on the proportion of medium and large-sized vehicles and the preset relationship table between vehicle type and influence value, the vehicle type is determined.

[0072] Here, the vehicle type is determined based on the proportion of medium and large vehicles and the preset relationship table between vehicle type and influence value.

[0073] In the context of vehicle-road cooperation, the distribution of vehicle types in the current road segment can be obtained in real time. If large vehicles account for a high proportion of the vehicle types, it will lead to a decrease in traffic flow between intersections, weakening the correlation between the two intersections. When the proportion of large vehicles in the traffic flow between intersections exceeds 20%, the impact on the road is significantly increased. Therefore, the range of vehicle type values ​​is as follows:

[0074] Table of Relationship between Vehicle Type and Impact Values

[0075] The proportion of medium and large vehicles r＜0.2 0.2≤r＜0.4 0.4≤r＜0.6 r≥0.6 Vehicle type value 1 0.8 0.6 0.4

[0076] Here, the corresponding value for weather is determined through the following steps: The value is determined based on the ratio of the average vehicle speed under normal weather conditions to the average vehicle speed under normal weather conditions. In poor visibility, drivers' average vehicle speed decreases, and vehicle acceleration significantly decreases compared to high visibility conditions. However, weather conditions are difficult to quantify as a correlation factor; therefore, the average speed of traffic between two intersections is used as an influencing factor, and the length of the time period needs to be determined through simulation. Therefore, the corresponding value for weather is: the ratio of the current average vehicle speed on the road segment to the average vehicle speed under normal weather conditions on the road segment. It should be noted that the upper limit for the weather value is 1. Because vehicle speeds between intersections vary with traffic flow, vehicle speeds are higher when traffic flow is low. In this case, the weather condition has a smaller impact on the correlation between the two intersections; therefore, the upper limit for the weather value is 1.

[0077] Here, the number of lanes is determined through the following steps: Based on the number of lanes in a road segment and a preset table showing the relationship between the number of lanes and their impact values, the corresponding number of lanes is determined. Increasing the number of lanes leads to increased traffic capacity on the road, more driving space for vehicles, and greater speed variations. Therefore, increasing the number of lanes has a certain impact on the connectivity between intersections. The range of lane numbers is shown in the table below:

[0078] Table showing the relationship between the number of lanes and the value of the impact.

[0079] Number of lanes j j＝1 j＝2 j≥3 The value of the number of lanes 1 0.95 0.9

[0080] S102: Input the first static traffic data and the first dynamic traffic flow data of any two intersections into the mathematical model of the correlation degree between intersections to determine the first correlation degree value between the two intersections.

[0081] In this step, the first static traffic data and the first dynamic traffic flow data of any two intersections are input into the mathematical model of the correlation degree between intersections to determine the first correlation degree value between the two intersections.

[0082] Here, the intersection correlation value refers to the strength of the connection between intersections. Intersections with strong correlation have similar traffic flow and congestion conditions. Assigning highly correlated intersections to the same sub-zone can improve the overall traffic volume of the road network. The intersection correlation model is the foundation of the road network sub-zone division algorithm, and establishing a suitable intersection correlation model can improve the effectiveness of road network sub-zone division.

[0083] In one possible implementation, for the first intersection and the second intersection, the step of inputting the first static traffic data and the first dynamic traffic flow data of any two intersections into the mathematical model of the correlation degree between the intersections to determine the first correlation degree value between the two intersections includes:

[0084] (1): The mathematical model of the correlation degree between intersections determines the first segment correlation degree value of the first intersection based on the first static traffic data and the first dynamic traffic flow data of the first intersection.

[0085] Here, the mathematical model of the correlation degree between intersections determines the first segment correlation degree value of the first intersection based on the first static traffic data and the first dynamic traffic flow data of the first intersection.

[0086] (2): The mathematical model of the correlation degree between intersections determines the correlation degree value of the second road segment of the second intersection based on the second static traffic data and the second dynamic traffic flow data of the second intersection.

[0087] Here, the mathematical model of intersection correlation determines the second segment correlation value of the second intersection based on the second static traffic data and the second dynamic traffic flow data of the second intersection.

[0088] (3): The mathematical model of the correlation degree between intersections determines the first correlation degree value between the first intersection and the second intersection based on the correlation degree value of the first road segment and the correlation degree value of the second road segment.

[0089] Here, the mathematical model of intersection correlation determines the first correlation value between the first intersection and the second intersection based on the correlation value of the first road segment and the correlation value of the second road segment.

[0090] The mathematical model for the correlation between intersections is as follows:

[0091]

[0092] In the formula, I represents the correlation degree between the two intersections, I1 represents the correlation degree value of the first road segment of the first intersection, and I2 represents the correlation degree value of the second road segment of the second intersection; T1 and T2 represent the average time for vehicles to pass through the corresponding road segment of the first intersection and the average time for vehicles to pass through the corresponding road segment of the second intersection, respectively; a is a discrete constant with a value of 0.1; c1 and c2 represent the factors of the traffic flow cycle of the first intersection and the traffic flow cycle of the second intersection, respectively; p1 and p2 represent the factors of the vehicle type of the first intersection and the second intersection, respectively; k1 and k2 represent the factors of the number of lanes of the first intersection and the second intersection, respectively; w1 and w2 represent the factors of the weather of the first intersection and the second intersection, respectively; q represents the correlation degree between the two intersections. cur1 q cur2 These represent the traffic flow from the upstream intersection to the first intersection and the traffic flow from the upstream intersection to the second intersection, respectively. qi1 , Σ qi2 These represent the total traffic flow heading towards the first intersection and the total traffic flow heading towards the second intersection within a preset time period, respectively. w1 and w2 are related to the average speed of the current lane.

[0093] S103: Based on multiple first correlation values, the urban road network to be divided into sub-regions.

[0094] In this step, the urban road network to be divided into sub-regions is determined based on multiple first correlation values.

[0095] In one possible implementation, the step of dividing the urban road network to be divided into sub-regions based on multiple first correlation values ​​includes:

[0096] I: The reciprocal of the first correlation value between any two intersections is determined as the connectivity weight between any two intersections in the urban road network to be divided.

[0097] Here, the reciprocal of the first correlation value between any two intersections is determined as the connectivity weight between any two intersections in the urban road network to be divided.

[0098] II: For the first intersection, remove the connecting edge corresponding to the maximum connectivity weight of the first intersection in the urban road network to be divided, and check whether the number of removals reaches a preset threshold.

[0099] Here, for the first intersection, the connecting edge corresponding to the maximum connectivity weight of the first intersection is removed in the urban road network to be divided, and it is checked whether the number of removals reaches a preset threshold.

[0100] III: If not, continue removing the connecting edge corresponding to the maximum connecting edge weight from the remaining connecting edge weights of the first intersection until the number of removals reaches the preset threshold, then stop removing the connecting edge of the first intersection, and divide the intersection currently connected to the first intersection and the first intersection into the same sub-region.

[0101] If not, then continue removing the connecting edge corresponding to the maximum connecting edge weight from the remaining connecting edge weights of the first intersection until the number of removals reaches a preset threshold, at which point stop removing the connecting edge of the first intersection, and divide the current intersection connected to the first intersection and the first intersection into the same sub-region.

[0102] The existing intersection correlation degree is a static value, so the sub-division result after the road network is divided cannot be changed and is also static. However, in reality, many factors affect the correlation between two intersections, such as traffic flow, vehicle type distribution, weather conditions (visibility), and the distance between the two intersections. In the context of vehicle-road cooperation, traffic flow information, vehicle type information, and weather information in the road network can be obtained in real time or near real time. Therefore, a dynamic intersection correlation degree model can be established to achieve dynamic road network sub-division. Dynamic sub-division can change the current sub-division result according to changes in the current traffic flow and other states in the road network.

[0103] This application provides a method for sub-region division of an urban road network. The method includes: acquiring first static traffic data and first dynamic traffic flow data for each intersection in the urban road network to be divided in a vehicle-road cooperative system; inputting the first static traffic data and first dynamic traffic flow data of any two intersections into an intersection correlation mathematical model to determine a first correlation value between the two intersections; and dividing the urban road network into sub-regions based on multiple first correlation values. By collecting static traffic data and dynamic traffic flow data of intersections in real time in the vehicle-road cooperative system, the intersection correlation mathematical model obtains the correlation value between intersections based on the static traffic data and dynamic traffic flow data, and dynamically divides the road network into sub-regions based on the correlation value, thus improving the accuracy of road network sub-region division. This improves the coordination between vehicles and infrastructure in the vehicle-road cooperative system, thereby enhancing road traffic safety.

[0104] Please see Figure 2 , Figure 2This is a second flowchart illustrating a method for sub-region division of an urban road network provided in an embodiment of this application. Figure 2 As shown in the embodiments of this application, the sub-region division method includes:

[0105] S201: Obtain the first static traffic data and the first dynamic traffic flow data of each intersection in the urban road network to be divided in the vehicle-road cooperative system;

[0106] S202: Input the first static traffic data and the first dynamic traffic flow data of any two intersections into the mathematical model of the correlation degree between intersections to determine the first correlation degree value between the two intersections;

[0107] S203: Based on multiple first correlation values, the urban road network to be divided into sub-regions.

[0108] The descriptions of S201 to S203 can be referred to those of S101 to S103, and the same technical effect can be achieved, so they will not be elaborated further.

[0109] S204: Obtain the mean value of the second static traffic data and the mean value of the second dynamic traffic flow data for each intersection in the urban road network to be divided within a preset time period; the mathematical model of the correlation degree between intersections determines the second correlation degree value between every two intersections based on the mean value of the second static traffic data and the mean value of the second dynamic traffic flow data for each intersection; and updates the division of the sub-regions of the urban road network to be divided based on multiple second correlation degree values.

[0110] In this step, the average value of the second static traffic data and the average value of the second dynamic traffic flow data for each intersection in the urban road network to be divided within a preset time period are obtained. The mathematical model of intersection correlation determines the second correlation value between every two intersections based on the average values ​​of the second static traffic data and the second dynamic traffic flow data for each intersection. Based on multiple second correlation values, the sub-region division of the urban road network to be divided is updated. This achieves dynamic sub-region division, allowing the current sub-region division results to be changed according to changes in traffic flow and other states within the current road network.

[0111] This solution utilizes real-time traffic data acquired from the vehicle-road system to achieve dynamic road network sub-zone division. Taking traffic flow as an example, traffic flow changes dynamically at different times, directly affecting the correlation between two intersections. Therefore, traditional solutions using the average traffic flow as the intersection correlation calculation value fail to reflect this dynamism. The proposed dynamic sub-zone division method transforms traffic flow, travel time, weather factors, and vehicle type distribution into dynamically changing values, collecting data every 15 minutes to calculate intersection correlation values ​​that more closely reflect the current traffic conditions.

[0112] This application provides a method for sub-region division of an urban road network. The sub-region division method includes: acquiring first static traffic data and first dynamic traffic flow data of each intersection in the urban road network to be divided in a vehicle-road cooperative system; inputting the first static traffic data and first dynamic traffic flow data of any two intersections into an intersection correlation mathematical model to determine a first correlation value between the two intersections; and dividing the urban road network to be divided into sub-regions based on multiple first correlation values. The method also includes acquiring the mean of second static traffic data and the mean of second dynamic traffic flow data of each intersection in the urban road network to be divided at the current time within a preset time period; determining a second correlation value between every two intersections based on the mean of the second static traffic data and the mean of the second dynamic traffic flow data of each intersection; and updating the sub-region division of the urban road network to be divided based on multiple second correlation values. By collecting static traffic data and dynamic traffic flow data of intersections in real time in the vehicle-road cooperative system, the mathematical model of the correlation degree between intersections obtains the correlation degree value between intersections based on the static traffic data and dynamic traffic flow data. Based on the correlation degree value, the road network sub-regions are dynamically divided, which improves the accuracy of the road network sub-region division.

[0113] Please see Figure 3 , Figure 4 , Figure 3 One of the structural schematic diagrams of a sub-region division device for an urban road network provided in an embodiment of this application; Figure 4 This is a second schematic diagram of a sub-region division device for an urban road network provided in an embodiment of this application. Figure 3 As shown, the sub-region division device 300 for the urban road network includes:

[0114] The acquisition module 310 is used to acquire the first static traffic data and the first dynamic traffic flow data of each intersection in the urban road network to be divided in the vehicle-road cooperative system.

[0115] The determination module 320 is used to input the first static traffic data and the first dynamic traffic flow data of any two intersections into the mathematical model of the correlation degree between intersections, and determine the first correlation degree value between the two intersections.

[0116] The partitioning module 330 is used to divide the urban road network to be divided into sub-regions based on multiple first correlation values.

[0117] Furthermore, such as Figure 4 As shown, the sub-region division device 300 further includes an update module 340, which is used for:

[0118] Obtain the mean of the second static traffic data and the mean of the second dynamic traffic flow data for each intersection in the urban road network to be divided within a preset time period at the current time.

[0119] The mathematical model for the correlation degree between intersections determines the second correlation degree value between every two intersections based on the mean of the second static traffic data and the mean of the second dynamic traffic flow data for each intersection.

[0120] The sub-regions of the urban road network to be divided are updated based on multiple second correlation values.

[0121] Furthermore, when determining the first correlation value between the two intersections by inputting the first static traffic data and the first dynamic traffic flow data of any two intersections into the mathematical model of the correlation degree between the intersections, the determining module 320 is specifically used for:

[0122] The mathematical model for the correlation degree between intersections determines the first segment correlation degree value of the first intersection based on the first static traffic data and the first dynamic traffic flow data of the first intersection.

[0123] The mathematical model for the correlation degree between intersections determines the correlation degree value of the second road segment of the second intersection based on the second static traffic data and the second dynamic traffic flow data of the second intersection.

[0124] The mathematical model for the correlation degree between intersections determines the first correlation degree value between the first intersection and the second intersection based on the correlation degree value of the first road segment and the correlation degree value of the second road segment.

[0125] Furthermore, when the partitioning module 330 is used to divide the urban road network to be divided into sub-regions based on multiple first correlation values, the partitioning module 330 is specifically used for:

[0126] The reciprocal of the first correlation value between any two intersections is determined as the connectivity weight between any two intersections in the urban road network to be divided.

[0127] For the first intersection, the connecting edge corresponding to the maximum connectivity weight of the first intersection is removed in the urban road network to be divided, and it is detected whether the number of removals reaches a preset threshold.

[0128] If not, continue removing the connecting edge corresponding to the maximum connecting edge weight from the remaining connecting edge weights of the first intersection until the number of removals reaches the preset threshold, at which point the removal of connecting edges of the first intersection is stopped, and the intersection currently connected to the first intersection and the first intersection are assigned to the same sub-region.

[0129] Furthermore, the acquisition module 310 determines the traffic flow cycle through the following steps:

[0130] The first historical average traffic flow on weekdays and the second historical average traffic flow on non-weekdays were determined for the road segment.

[0131] If the current time is a weekday, the traffic flow cycle is determined based on the ratio of the real-time traffic flow of vehicles on the road segment to the first historical average traffic flow.

[0132] If the current time is a non-working day, the traffic flow cycle is determined based on the ratio of the real-time traffic flow of vehicles on the road segment to the second historical average traffic flow.

[0133] Furthermore, the acquisition module 310 determines the vehicle type through the following steps:

[0134] Obtain the total number of various vehicle types passing through the road segment;

[0135] Based on the total number of various vehicle types and the number of medium and large vehicles, the proportion of medium and large vehicles in each road segment is determined.

[0136] The vehicle type is determined based on the proportion of medium and large-sized vehicles and the preset relationship table between vehicle type and influence value.

[0137] This application provides a sub-region division device for urban road networks. The device includes: an acquisition module for acquiring first static traffic data and first dynamic traffic flow data of each intersection in the urban road network to be divided within a vehicle-road cooperative system; a determination module for inputting the first static traffic data and first dynamic traffic flow data of any two intersections into an intersection correlation mathematical model to determine a first correlation value between the two intersections; and a partitioning module for dividing the urban road network into sub-regions based on multiple first correlation values. By collecting static traffic data and dynamic traffic flow data of intersections in real time within the vehicle-road cooperative system, the intersection correlation mathematical model obtains the correlation value between intersections based on the static traffic data and dynamic traffic flow data, and dynamically divides the road network into sub-regions based on the correlation value, thus improving the accuracy of road network sub-region division. This enhances the coordination between vehicles and infrastructure in the vehicle-road cooperative system, thereby improving road traffic safety.

[0138] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.

[0139] The memory 520 stores machine-readable instructions executable by the processor 510. When the electronic device 500 is running, the processor 510 and the memory 520 communicate via the bus 530. When the machine-readable instructions are executed by the processor 510, they can perform the operations described above. Figure 1 as well as Figure 2 The steps of the sub-region division method of the urban road network in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0140] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 as well as Figure 2 The steps of the sub-region division method of the urban road network in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0141] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0142] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus 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. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0143] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple urban road network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0144] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0145] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or urban road network equipment, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0146] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A sub-region division method of an urban road network, characterized by, The sub-region division method comprises: In a cooperative vehicle infrastructure system, first static traffic data and first dynamic traffic flow data of each intersection in a city road network to be divided are acquired; The first static traffic data and the first dynamic traffic flow data of any two intersections are input into an intersection correlation degree mathematical model to determine a first correlation degree value between the two intersections; The intersection correlation degree mathematical model is as follows: ； In the formula, I is the correlation degree between two intersections, I1 is the first road section correlation degree value of the first intersection, and I2 is the second road section correlation degree value of the second intersection; T1 and T2 are the average time of a vehicle passing through the corresponding road section of the first intersection and the average time of a vehicle passing through the corresponding road section of the second intersection, respectively; a is a dispersion constant, and the value is 0.1; c1 and c2 are the factors of the traffic cycle of the first intersection and the factors of the traffic cycle of the second intersection, respectively; p1 and p2 are the factors of the vehicle type of the first intersection and the second intersection, respectively; k1 and k2 are the factors of the number of lanes of the first intersection and the second intersection, respectively; w1 and w2 are the factors of the weather of the first intersection and the second intersection, respectively; q cur1 , q cur2 are the traffic flow of the upstream intersection of the road section to the first intersection and the traffic flow of the upstream intersection of the road section to the second intersection, respectively; Σ qi1 , Σ qi2 are the total traffic flow to the first intersection in a preset time period and the total traffic flow to the second intersection in a preset time period, respectively; wherein w1 and w2 are the average speed of the current lane. Based on a plurality of the first correlation degree values, the city road network to be divided is sub-regionally divided, comprising: The reciprocal of the first correlation degree value between each two intersections is determined as a connection degree weight value between each two intersections in the city road network to be divided; For a first intersection, a connection edge corresponding to a maximum connection degree weight value of the first intersection is removed in the city road network to be divided, and whether the removal times reach a preset threshold is detected; If not, the connection edge corresponding to the maximum connection degree weight value is continuously removed from the remaining connection degree weight values of the first intersection until the removal times reach the preset threshold, at which point the removal of the connection edge of the first intersection is stopped, and the intersection connected to the first intersection and the first intersection are divided into the same sub-region.

2. The sub-region division method according to claim 1, characterized in that, After the city road network to be divided is sub-regionally divided based on a plurality of the first correlation degree values, the sub-region division method further comprises: The average of second static traffic data and the average of second dynamic traffic flow data of each intersection in the city road network to be divided at a current time in a preset time period are acquired; The intersection correlation degree mathematical model determines a second correlation degree value between each two intersections based on the average of the second static traffic data and the average of the second dynamic traffic flow data of each intersection; Based on a plurality of the second correlation degree values, the division of the sub-regions of the city road network to be divided is updated.

3. The sub-region division method of claim 1, wherein, For a first intersection and a second intersection, the first static traffic data and the first dynamic traffic flow data of any two intersections are input into an intersection correlation degree mathematical model to determine a first correlation degree value between the two intersections, comprising: The intersection correlation degree mathematical model determines a first road section correlation degree value of the first intersection according to the first static traffic data and the first dynamic traffic flow data of the first intersection; The intersection correlation degree mathematical model determines a second road section correlation degree value of the second intersection according to the second static traffic data and the second dynamic traffic flow data of the second intersection; The intersection correlation degree mathematical model determines a first correlation degree value between the first intersection and the second intersection according to the first road section correlation degree value and the second road section correlation degree value.

4. The sub-region division method of claim 1, wherein, The first dynamic traffic flow data comprises: The average time of the vehicle passing through the road section corresponding to the intersection, the traffic cycle of the road section corresponding to the intersection, the vehicle type of the road section corresponding to the intersection, the weather of the road section corresponding to the intersection, the traffic volume of the upstream intersection of the road section to the intersection, the total traffic volume to the intersection in a preset time period, and the average driving speed of the road section of the intersection.

5. The sub-region division method according to claim 4, characterized in that, The traffic cycle is determined by the following steps: Determine the first historical average traffic volume of the vehicle on the road section on weekdays and the second historical average traffic volume of the vehicle on the road section on non-working days; If the current time is a weekday, the traffic cycle is determined based on the ratio of the real-time traffic volume of the vehicle on the road section to the first historical average traffic volume; If the current time is a non-working day, the traffic cycle is determined based on the ratio of the real-time traffic volume of the vehicle on the road section to the second historical average traffic volume.

6. The sub-region division method of claim 4, wherein, The vehicle type is determined by the following steps: Obtain the total number of vehicles of various types passing through the road section; Determine the proportion of the number of medium and large vehicles on the road section based on the total number of vehicles of various types and the number of medium and large vehicles; Determine the vehicle type based on the proportion of the number of medium and large vehicles and a preset vehicle type and influence value relationship table.

7. A sub-region division device for an urban road network, characterized in that, The sub-area division device comprises: An acquisition module configured to acquire first static traffic data and first dynamic traffic flow data of each intersection in a city road network to be divided in a vehicle-road cooperation system; A determination module configured to input the first static traffic data and the first dynamic traffic flow data of any two intersections into an intersection correlation mathematical model to determine a first correlation value between the two intersections; The intersection correlation mathematical model is as follows: ； In the formula, I is the correlation degree between two intersections, I1 is the first road section correlation degree value of the first intersection, and I2 is the second road section correlation degree value of the second intersection; T1 and T2 are the average time of a vehicle passing through the corresponding road section of the first intersection and the average time of a vehicle passing through the corresponding road section of the second intersection, respectively; a is a dispersion constant, and the value is 0.1; c1 and c2 are the factors of the traffic cycle of the first intersection and the factors of the traffic cycle of the second intersection, respectively; p1 and p2 are the factors of the vehicle type of the first intersection and the second intersection, respectively; k1 and k2 are the factors of the number of lanes of the first intersection and the second intersection, respectively; w1 and w2 are the factors of the weather of the first intersection and the second intersection, respectively; q cur1 , q cur2 are the traffic flow of the upstream intersection of the road section to the first intersection and the traffic flow of the upstream intersection of the road section to the second intersection, respectively; Σ qi1 , Σ qi2 are the total traffic flow to the first intersection in a preset time period and the total traffic flow to the second intersection in a preset time period, respectively; wherein w1 and w2 are the average speed of the current lane. A partition module configured to divide the city road network to be divided into sub-areas based on a plurality of first correlation values, comprising: Determine the reciprocal of the first correlation value between each two intersections as a connection degree weight value between each two intersections in the city road network to be divided; For a first intersection, remove a connection edge corresponding to the maximum connection degree weight value of the first intersection in the city road network to be divided, and detect whether the removal frequency reaches a preset threshold value; If not, continue to remove the connection edge corresponding to the maximum connection degree weight value in the remaining connection degree weight values of the first intersection until the removal frequency reaches the preset threshold value, stop removing the connection edge of the first intersection, and divide the intersection connected to the first intersection and the first intersection into the same sub-area.

8. An electronic device, comprising: Comprise: A processor, a memory, and a bus, the memory stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, the machine readable instructions are executed by the processor to execute the steps of the sub-area division method of the city road network as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, the computer program is executed by the processor to execute the steps of the sub-area division method of the city road network as claimed in any one of claims 1 to 6.

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