Traffic control method and device, computer device and storage medium

Abstract

The application relates to a traffic control method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring traffic information of each intersection node, wherein the traffic information comprises vehicle position information; determining traffic environment information according to the traffic information of each intersection node, and generating driving route prediction information of a vehicle, wherein the traffic environment information comprises a congested intersection node; planning a vehicle guide route according to the driving route prediction information and the traffic environment information; and adjusting a traffic light of each intersection node based on the vehicle guide route. The method can relieve traffic congestion.

Description

Technical Field

[0001] This application relates to the field of traffic control technology, and in particular to a traffic control method, apparatus, computer equipment, and storage medium. Background Technology

[0002] With the rapid increase in the number of motor vehicles in cities, traffic congestion has become increasingly serious, and existing traffic control methods are no longer able to alleviate this growing problem. Summary of the Invention

[0003] Therefore, it is necessary to provide a traffic control method, device, computer equipment, and storage medium to address the aforementioned technical problems and alleviate traffic congestion.

[0004] Firstly, this application provides a traffic control method. The traffic control method includes:

[0005] Obtain traffic information at each intersection node, wherein the traffic information includes vehicle location information;

[0006] Traffic environment information is determined based on the traffic information of each intersection node, and vehicle travel route prediction information is generated, wherein the traffic environment information includes congested intersection nodes.

[0007] Plan vehicle guidance routes based on the predicted driving routes and the traffic environment information;

[0008] The traffic lights at each intersection node are adjusted based on the vehicle guidance route.

[0009] In one embodiment, obtaining traffic information at each intersection node includes:

[0010] Obtain the image information uploaded by each of the aforementioned intersection nodes;

[0011] The traffic information of each intersection node is obtained based on the image information.

[0012] In one embodiment, determining traffic environment information based on the traffic information of each intersection node and generating vehicle route prediction information includes:

[0013] Based on the traffic information, identify the primary road intersections where congestion is present;

[0014] Obtain each secondary intersection node based on each of the primary intersection nodes;

[0015] Obtain all tertiary intersection nodes other than the aforementioned primary intersection nodes and secondary intersection nodes;

[0016] The traffic environment information is determined based on each of the first-level intersection nodes, each of the second-level intersection nodes, and each of the third-level intersection nodes.

[0017] In one embodiment, obtaining each secondary intersection node based on each of the primary intersection nodes includes:

[0018] Each intersection node that is adjacent to the first-level intersection node and has road connectivity is identified as the second-level intersection node.

[0019] In one embodiment, obtaining each secondary intersection node based on each of the primary intersection nodes includes:

[0020] If the road passing through the primary intersection node includes an urban arterial road, the target intersection node located on the urban arterial road is designated as a secondary intersection node, wherein the target intersection node is adjacent to the secondary intersection node located on the same urban arterial road.

[0021] In one embodiment, the generation of vehicle route prediction information includes:

[0022] The driving direction of each vehicle is determined based on the location information of each vehicle within the predetermined time period;

[0023] The vehicle's travel route prediction information is determined based on the travel direction of each vehicle.

[0024] In one embodiment, the step of planning the vehicle guidance route based on the driving route prediction information and the traffic environment information includes:

[0025] The alternative intersection nodes for each of the first-level intersection nodes, the second-level intersection nodes, the third-level intersection nodes, and the driving route prediction information are obtained, wherein the alternative intersection nodes include at least one of the second-level intersection nodes.

[0026] The vehicle guidance route is planned based on each of the alternative intersection nodes and the driving route prediction information.

[0027] In one embodiment, the step of obtaining the primary intersection nodes experiencing congestion based on the traffic information includes:

[0028] Obtain the distance traveled by vehicles at each intersection node during a single traffic cycle;

[0029] The intersection nodes where the travel distance of the first n vehicles in a single traffic cycle is less than a preset threshold are identified as the first-level intersection nodes.

[0030] In one embodiment, the method further includes:

[0031] If a pedestrian is detected to maintain an abnormal posture on a road where vehicles are traveling for a period of time that reaches a first preset time, an alarm message will be sent.

[0032] If a pedestrian is detected to maintain an abnormal posture on a road for a period of time that reaches a second preset time, an emergency medical information will be sent.

[0033] In one embodiment, the method further includes:

[0034] Pedestrians who maintain an abnormal posture for a period of time up to the second preset time are identified as accident victims;

[0035] Collect facial images of the accident victims and obtain and send their identity information.

[0036] In one embodiment, the method further includes:

[0037] Obtain driving reference data, wherein the driving reference data includes at least one of historical vehicle driving data and driving planning data uploaded by associated terminals;

[0038] The driving route prediction information is adjusted based on the driving reference data.

[0039] Secondly, this application also provides a traffic control device. The device includes:

[0040] The traffic information acquisition module is used to acquire traffic information at each intersection node;

[0041] The traffic environment information determination module is used to determine traffic environment information based on the traffic information of each intersection node and generate vehicle travel route prediction information.

[0042] The vehicle guidance route planning module is used to plan vehicle guidance routes based on the driving route prediction information and the traffic environment information.

[0043] The traffic light adjustment module is used to adjust the traffic lights at each intersection node based on the vehicle guidance route.

[0044] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0045] Obtain traffic information at each intersection node, wherein the traffic information includes vehicle location information;

[0046] Traffic environment information is determined based on the traffic information of each intersection node, and vehicle travel route prediction information is generated, wherein the traffic environment information includes congested intersection nodes.

[0047] Plan vehicle guidance routes based on the predicted driving routes and the traffic environment information;

[0048] The traffic lights at each intersection node are adjusted based on the vehicle guidance route.

[0049] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0050] Obtain traffic information at each intersection node, wherein the traffic information includes vehicle location information;

[0051] Traffic environment information is determined based on the traffic information of each intersection node, and vehicle travel route prediction information is generated, wherein the traffic environment information includes congested intersection nodes.

[0052] Plan vehicle guidance routes based on the predicted driving routes and the traffic environment information;

[0053] The traffic lights at each intersection node are adjusted based on the vehicle guidance route.

[0054] The aforementioned traffic control method, device, computer equipment, and storage medium acquire traffic information from various intersection nodes, including vehicle location information. Based on the acquired vehicle location information from each intersection node, traffic environment information can be determined. Congested intersection nodes can be identified using the vehicle location information. Furthermore, vehicle location information can be used to predict vehicle routes and generate vehicle route prediction information. Based on the determined traffic environment information and the generated vehicle route prediction information, guidance routes for vehicles at each intersection node can be planned. Then, the traffic lights at each intersection node are adjusted according to the planned guidance routes to guide vehicle movement, achieve vehicle diversion, and alleviate traffic congestion. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating a traffic control method in one embodiment;

[0056] Figure 2 This is a diagram illustrating the application environment of a traffic control method in one embodiment;

[0057] Figure 3 This is a flowchart illustrating step S101 in one embodiment;

[0058] Figure 4 This is a flowchart illustrating step S102 in one embodiment;

[0059] Figure 5 This is a diagram illustrating the application environment of the traffic control method in another embodiment;

[0060] Figure 6 This is a flowchart illustrating step S102 in another embodiment;

[0061] Figure 7 This is a flowchart illustrating step S103 in one embodiment;

[0062] Figure 8 This is a flowchart illustrating step S401 in one embodiment;

[0063] Figure 9 This is a flowchart illustrating the traffic control method in another embodiment;

[0064] Figure 10 This is a flowchart illustrating the traffic control method in yet another embodiment;

[0065] Figure 11 This is a flowchart illustrating the traffic control method in yet another embodiment;

[0066] Figure 12 This is a structural block diagram of a traffic control device in one embodiment;

[0067] Figure 13 This is an internal structural diagram of a computer device in one embodiment.

[0068] Explanation of reference numerals in the attached figures:

[0069] 1201 - Traffic Information Acquisition Module, 1202 - Traffic Environment Information Determination Module, 1203 - Vehicle Guidance Route Planning Module, 1204 - Traffic Light Adjustment Module. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0071] In one embodiment, such as Figure 1 As shown, this application provides a traffic control method. It is understood that this traffic control method can be applied to a processing terminal, which can be a computer, laptop, tablet, etc. This traffic control method can also be applied to a server, and further to a system including a processing terminal and a server, and is implemented through the interaction between the processing terminal and the server. Taking the application of this traffic control method to a processing terminal as an example, the traffic control method includes:

[0072] S101: Obtain traffic information for each intersection node, including vehicle location information.

[0073] Each intersection node can be equipped with sensors to acquire traffic information, such as image sensors, to obtain vehicle location information at each intersection node.

[0074] S102: Determine traffic environment information based on traffic information at each intersection node, and generate vehicle route prediction information, including congested intersection nodes.

[0075] By acquiring traffic information from various intersection nodes and analyzing the location information of each vehicle traveling or parked at each intersection node, congested and non-congested intersection nodes can be identified, thereby determining traffic environment information. Furthermore, by analyzing the location information of each vehicle traveling or parked at each intersection node, the driving direction and route of each vehicle can be predicted, generating vehicle route prediction information.

[0076] S103: Plan vehicle guidance routes based on driving route prediction information and traffic environment information.

[0077] Among them, traffic environment information and driving route prediction information determined and generated based on the analysis of traffic information at each intersection node, and based on the predicted driving direction and driving route of each vehicle and the obtained intersection nodes with congestion, can plan guidance routes for vehicles.

[0078] S104: Adjust the traffic lights at each intersection node based on the vehicle guidance route.

[0079] Based on the planned vehicle guidance route, the duration of the red and green lights at each intersection can be adjusted. For example, such as... Figure 2As shown, congestion has occurred at intersection node L1. The traffic lights at intersection node L1 can be turned off, and the location information of the congested intersection node can be sent to the corresponding traffic control system. This allows the traffic control system to dispatch traffic police to the congested intersection node L1 to direct vehicles and alleviate congestion more quickly, preventing a complete gridlock at intersection node L1. For intersection node L2-1 surrounding intersection node L1, the direction of traffic can be extended. The red light duration of the traffic light at intersection node L1 is extended, while the green light duration of the traffic light directing vehicles to other intersection nodes such as intersection nodes L2-5 and L2-8 is also extended. For intersection node L2-2 surrounding intersection node L1, the red light duration of the traffic light directing vehicles to intersection node L1 is extended, while the green light duration of the traffic light directing vehicles to other intersection nodes such as intersection nodes L2-5 and L2-6 is also extended, in order to guide traffic diversion and alleviate traffic pressure at intersection node L1.

[0080] The aforementioned traffic control method acquires traffic information from various intersection nodes, including vehicle location information. Based on the vehicle location information at each intersection node, traffic environment information can be determined, and congested intersection nodes can be identified using the vehicle location information. Furthermore, vehicle location information can be used to predict vehicle routes and generate vehicle route prediction information. Using the determined traffic environment information and the generated vehicle route prediction information, guidance routes for vehicles at each intersection node can be planned. Then, the traffic lights at each intersection node are adjusted according to the planned guidance routes to guide vehicle traffic, achieve vehicle diversion, and alleviate traffic congestion.

[0081] In one embodiment, such as Figure 3 As shown, traffic information for each intersection node is obtained, including:

[0082] S301: Obtain image information uploaded by each intersection node.

[0083] It is understandable that each intersection node can be equipped with camera devices to monitor road conditions and the situation of vehicles and pedestrians. Therefore, by acquiring vehicle traffic images and pedestrian traffic images captured by the camera devices at each intersection node, image information of each intersection node can be obtained.

[0084] S302: Obtain traffic information for each intersection node based on image information.

[0085] Based on the acquired image information, the location information of each vehicle traveling or parked at the intersection node, the number of vehicles traveling or parked at the intersection node, and the location information of pedestrians crossing the intersection node can be analyzed. Therefore, traffic information of each intersection node can be obtained based on the analysis of image information.

[0086] In one embodiment, such as Figure 4 As shown, traffic environment information is determined based on traffic information at each intersection node, and vehicle route prediction information is generated, including:

[0087] S401: Based on traffic information, identify all primary road intersections where congestion is present.

[0088] Based on the location and number of vehicles traveling or stopped at each intersection node, the traffic information at each intersection node can be used to determine which intersection nodes are experiencing traffic congestion. These congested intersection nodes are then classified as Level 1 intersection nodes. The location information of each Level 1 intersection node is then sent to the traffic control system to facilitate the deployment of traffic police to alleviate congestion. For example, such as... Figure 5 As shown, if intersection node L1 is a congested intersection node, then intersection node L1 will be set as a first-level intersection node. At the same time, the location information of intersection node L1 will be sent to the traffic command system so that the traffic command system can arrange traffic police to clear the congested intersection node L1. The traffic lights at intersection node L1 can be temporarily stopped, and the traffic police will coordinate the vehicles at intersection node L1.

[0089] S402: Obtain the secondary intersection nodes based on the primary intersection nodes.

[0090] In this context, the distance between a secondary intersection node and a primary intersection node is less than a preset distance threshold. The first preset distance threshold can be the distance between a primary intersection node and the farthest intersection node that is adjacent to and connected to it by a road. After identifying each primary intersection node experiencing congestion, intersection nodes within a preset distance threshold range from each primary intersection node can be designated as secondary intersection nodes. For example, if intersection node L1 is a congested intersection node, then L1 is designated as a primary intersection node. Among the intersection nodes L2-1, L2-2, L2-3, and L2-4 adjacent to and connected to L1, L2-4 is the farthest from L1. Therefore, with L1 as the center and the preset distance threshold (the distance between L2-4 and L1) as the radius, the area within the preset distance threshold range surrounding L1 is designated as a secondary intersection node. All intersection nodes are classified as secondary intersection nodes. If the straight-line distance between intersection nodes L2-8 and L3-6 and intersection node L1 is greater than a preset distance threshold, and the straight-line distance between intersection nodes L2-7, L2-6, and L2-5 and intersection node L1 is less than a preset distance threshold, then intersection nodes L2-1, L2-2, L2-3, L2-4, L2-5, L2-6, and L2-7 are classified as secondary intersection nodes. For traffic lights at secondary intersection nodes, the red light time of the signal light directing vehicles to primary intersection nodes is extended, and the green light time of the signal light directing vehicles to non-primary intersection nodes is also extended.

[0091] S403: Obtain all tertiary intersection nodes except for each primary intersection node and each secondary intersection node.

[0092] After determining the primary and secondary intersection nodes, the remaining intersection nodes are classified as tertiary intersection nodes. The traffic lights at each tertiary intersection node operate according to a pre-set timing scheme. For example, if intersection node L1 is a congested intersection node, then intersection node L1 is designated as a primary intersection node. Intersection nodes L2-1, L2-2, L2-3, L2-4, L2-5, L2-6, and L2-7 are designated as secondary intersection nodes. Then, intersection nodes L2-8, L3-1, L3-2, L3-3, L3-4, L3-5, L3-6, and L3-7 are designated as tertiary intersection nodes. The traffic lights at these tertiary intersection nodes operate according to the pre-set timing scheme.

[0093] In the application, higher-level intersection nodes can cover lower-level intersection nodes. Specifically, Level 1 intersection nodes are higher in level than Level 2 and Level 3 intersection nodes, and Level 2 intersection nodes are higher in level than Level 3 intersection nodes. If a Level 2 intersection node within a preset distance threshold range centered on a Level 1 intersection node also experiences congestion, then that Level 2 intersection node is classified as a Level 1 intersection node.

[0094] For example, if intersection node L1 is a first-level intersection node and the distance between each adjacent intersection node is the same, then theoretically intersection nodes L2-1, L2-2, L2-3 and L2-4 are second-level intersection nodes, and intersection nodes L2-5, L2-6, L2-7, L2-8, L3-1, L3-2, L3-3, L3-4, L3-5, L3-6 and L3-7 are third-level intersection nodes. If traffic congestion also occurs at intersection node L2-3, then intersection node L2-3 will also be classified as a Level 1 intersection node. At the same time, the other intersection nodes will be reclassified. In this case, nodes L2-1, L2-2, L2-4, L2-7, L2-6, and L3-6 will be Level 2 intersection nodes; and nodes L2-5, L2-8, L3-1, L3-2, L3-3, L3-4, L3-5, and L3-7 will be Level 3 intersection nodes.

[0095] S404: Determine traffic environment information based on each primary intersection node, each secondary intersection node, and each tertiary intersection node.

[0096] Based on the designated primary, secondary, and tertiary intersection nodes, a three-tiered traffic network system can be constructed. Furthermore, based on the traffic information from each primary, secondary, and tertiary intersection node, the city's traffic environment information can be comprehensively determined.

[0097] In one embodiment, obtaining each secondary intersection node based on the primary intersection node includes the step of determining each intersection node that is adjacent to the primary intersection node and has road connectivity as a secondary intersection node.

[0098] For example, if intersection node L1 is a first-level intersection node, and intersection nodes L2-1, L2-2, L2-3, and L2-4 are adjacent to intersection node L1 and connected by a road, then intersection nodes L2-1, L2-2, L2-3, and L2-4 are determined as second-level intersection nodes.

[0099] In one embodiment, obtaining secondary intersection nodes based on primary intersection nodes includes: if the road passing through the primary intersection node includes an urban arterial road, then identifying the target intersection node located on the urban arterial road as a secondary intersection node. The target intersection node is adjacent to any secondary intersection node located on the same urban arterial road.

[0100] For example, such as Figure 5 As shown, if intersection node L1 is a first-level intersection node, and intersection nodes L2-1, L2-2, L2-3, and L2-4 are adjacent to intersection node L1 and connected by roads, and the roads between intersection nodes L2-1, L2-2, L2-3, and L2-4 and intersection node L1 are not urban arterial roads, then intersection nodes L2-1, L2-2, L2-3, and L2-4 are directly connected to intersection node L1. Nodes 2-3 and L2-4 are designated as secondary intersection nodes. If the road between intersection node L2-3 and intersection node L1 is an urban arterial road, then intersection nodes L2-1, L2-2, L2-3, and L2-4 are first designated as secondary intersection nodes. Then, intersection nodes along the direction of the urban arterial road that are adjacent to intersection nodes L2-1 and L2-3, such as intersection node L3-6, are designated as secondary intersection nodes.

[0101] In one embodiment, such as Figure 6 As shown, the generated vehicle route prediction information includes:

[0102] S601: Determine the driving direction of each vehicle based on the location information of each vehicle within a predetermined time period.

[0103] The direction of vehicle movement can be determined based on the vehicle's location information at different times, and the driving direction of each vehicle can be determined based on the vehicle's location information within a predetermined time period.

[0104] S602: Determine the vehicle's travel route prediction information based on the travel direction of each vehicle.

[0105] By analyzing the acquired image information, the location information of each vehicle traveling or parked on straight-ahead roads, left-turn roads, and right-turn roads within a predetermined time period can be determined, thus identifying the possible travel routes of each vehicle. For example, if a vehicle is located on a straight-ahead road throughout the predetermined time period, its travel direction can be determined to be straight. After determining the travel direction of each vehicle, its subsequent travel route can be predicted based on the road it is located on.

[0106] For example, such as Figure 5As shown, a vehicle is within the intersection node L2-3. Based on the acquired image information, it can be determined that the vehicle's direction of travel within a predetermined time period is from intersection node L3-6 to intersection node L2-3. Furthermore, the vehicle remains on the straight road within the predetermined time period. Therefore, it can be predicted that the vehicle will head towards intersection node L1. Consequently, the predicted routes for the vehicle are: intersection node L2-3 to intersection node L1 to intersection node L2-1, intersection node L2-3 to intersection node L1 to intersection node L2-4, and intersection node L2-3 to intersection node L1 to intersection node L2-2.

[0107] In one embodiment, such as Figure 7 As shown, vehicle guidance routes are planned based on driving route prediction information and traffic environment information, including:

[0108] S701: Obtain alternative intersection nodes for each primary intersection node based on each primary intersection node, each secondary intersection node, each tertiary intersection node, and driving route prediction information. The alternative intersection nodes include at least one secondary intersection node.

[0109] If intersection node L1 is congested and is classified as a Level 1 intersection node, and the adjacent intersection nodes L2-1, L2-2, L2-3, and L2-4, which are connected to intersection node L1 by roads, are not congested, and the roads connecting intersection nodes L2-1, L2-2, L2-3, and L2-4 to intersection node L1 are not urban arterial roads, then intersection nodes L2-1, L2-2, L2-3, and L2-4 will be designated as Level 2 intersection nodes. When it is predicted that some vehicles at the secondary intersection node L2-1 will travel from the primary intersection node L1 to the secondary intersection node L2-3, and given the congestion at the primary intersection node L1, alternative routes for these vehicles to reach intersection node L2-3 can be analyzed. These alternative routes include those starting from intersection node L2-1, passing through intersection nodes L2-8, L2-4, and L2-7 to reach intersection node L2-3, and those starting from intersection node L2-1, passing through intersection nodes L2-5, L2-2, and L2-6 to reach intersection node L2-3. Therefore, intersection nodes L2-8, L2-4, L2-7, L2-5, L2-2, and L2-6 can be identified as alternative intersection nodes.

[0110] S702: Plan vehicle guidance routes based on the predicted information of each alternative intersection node and driving route.

[0111] It is understandable that after determining the primary and secondary intersection nodes and the predicted vehicle routes, the timing scheme of the traffic lights at the secondary intersection nodes should be adjusted to guide vehicles to alternative intersection nodes and prevent vehicles from entering the primary intersection nodes, thereby alleviating traffic pressure. When it is predicted that some vehicles at the secondary intersection node L2-1 will travel through the primary intersection node L1 to the secondary intersection node L2-3, intersection nodes L2-8, L2-4, L2-7, L2-5, L2-2, and L2-6 can be identified as alternative intersection nodes. Extend the red light time of the traffic light at intersection node L2-1 that directs vehicles to intersection node L1, and simultaneously extend the green light time of the traffic light that directs vehicles to the traffic lights that replace intersection nodes L2-5 and L2-8. This allows vehicles to travel from intersection node L2-1 through intersection nodes L2-5, L2-2, and L2-6, or from intersection node L2-1 through intersection nodes L2-8, L2-4, and L2-7 to the designated intersection, thereby diverting traffic and alleviating traffic congestion.

[0112] In one embodiment, such as Figure 8 As shown, traffic information is used to identify primary intersections experiencing congestion, including:

[0113] S801: Obtain the distance traveled by vehicles at each intersection node during a single traffic cycle.

[0114] By obtaining the vehicle location information at each intersection node, the position movement information of the vehicles at each intersection node can be determined, and then the movement distance of the vehicles at each intersection node in a single traffic cycle can be obtained. A single traffic cycle can be two traffic light cycles.

[0115] S802: Identify intersection nodes where the travel distance of the first n vehicles in a single traffic cycle is less than a preset threshold as first-level intersection nodes.

[0116] If the distance traveled by the first n vehicles within a single traffic cycle at a given intersection node is less than a preset threshold, the intersection node is considered congested and is classified as a Level 1 intersection node. The preset threshold can be 5 meters. Similarly, if the distance traveled by the first two vehicles at intersection node L1 within two traffic light cycles is less than 5 meters, intersection node L1 is considered congested and is classified as a Level 1 intersection node.

[0117] In applications, the number of vehicles within the coverage area of ​​an intersection node can be obtained through image information. By comparing this number of vehicles with the node's capacity threshold, intersection nodes with a number of vehicles exceeding the threshold can be identified, indicating congestion and classifying them as Level 1 intersection nodes. For example, if the capacity threshold for intersection node L1 is 10 vehicles, and 12 vehicles are detected within L1, then L1 is considered congested and classified as a Level 1 intersection node.

[0118] In one embodiment, such as Figure 9 As shown, the traffic control method provided in this application also includes:

[0119] S901: If a pedestrian is detected to maintain an abnormal posture on a road for a period of time that reaches the first preset time, an alarm message will be sent.

[0120] When a pedestrian remains in an abnormal posture on a road for a period of time that exceeds a first preset time, it can be inferred that a traffic accident has occurred, and the pedestrian may be injured or obstructing traffic. An alarm message, including an image of the accident location, is sent to the traffic police system so that officers can quickly remove the pedestrian and prevent traffic chaos. The first preset time can be 30 seconds, and the abnormal posture can be either lying down or sitting.

[0121] S902: If a pedestrian is detected to maintain an abnormal posture on a road for a period of time that reaches the second preset time, an emergency rescue message will be sent.

[0122] If a pedestrian remains in an abnormal posture on a roadway for an extended period of time (up to a second preset time), it can be assumed that the pedestrian is injured. An emergency medical information alert is then sent to the emergency medical system to ensure that emergency personnel arrive at the scene as quickly as possible and protect the pedestrian's life. The second preset time can be 60 seconds.

[0123] In one embodiment, such as Figure 10 As shown, the traffic control method provided in this application also includes:

[0124] S1001: Pedestrians who maintain an abnormal posture for a period of time up to the second preset time are identified as accident victims.

[0125] S1002: Collect facial images of accident victims, obtain and send their identity information.

[0126] If a pedestrian maintains an abnormal posture for a period of time up to the second preset time, it can be determined that the pedestrian has been involved in a traffic accident, and the pedestrian can be identified as the person involved in the accident. At the same time, the facial image of the person involved in the accident is collected, and the identity information of the person involved in the accident is determined through the facial image. The identity information of the person involved in the accident is then sent to the emergency medical system so that the emergency medical system can arrange rescue measures.

[0127] In one embodiment, such as Figure 11 As shown, the traffic control method provided in this application also includes:

[0128] S1101: Obtain driving reference data, wherein the driving reference data includes at least one of historical vehicle driving data and driving planning data uploaded by associated terminals.

[0129] Historical vehicle driving data can be the vehicle driving data of each intersection node at the current time within a predetermined time period. For example, the predetermined time period is 30 days, the current time is 8:00 am, and there is an 80% probability that intersection node L1 will be congested at 8:00 am in the past 30 days. Driving planning data can include at least one of the destination information and driving route information uploaded by associated terminals.

[0130] S1102: Adjust the driving route prediction information based on driving reference data.

[0131] By acquiring historical vehicle driving data and driving plan data uploaded by associated terminals, the driving route prediction information can be comprehensively adjusted in real time by combining historical traffic flow data and driving plans of each intersection node. For example, if intersection node L1 has an 80% probability of congestion at 8:00 AM in the past 30 days, it can be directly identified as a primary intersection node. By combining destination information and driving route information uploaded by associated terminals, the timing of traffic lights at secondary intersection nodes can be adjusted to direct vehicles to non-primary intersection nodes and avoid traffic congestion.

[0132] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0133] Based on the same inventive concept, this application also provides a traffic control device for implementing the traffic control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in the traffic control device embodiments provided below can be found in the limitations of the traffic control method described above, and will not be repeated here.

[0134] In one embodiment, such as Figure 12 As shown, a traffic control device is provided, including: a traffic information acquisition module, a traffic environment information determination module, a vehicle guidance route planning module, and a traffic light adjustment module, wherein:

[0135] The traffic information acquisition module 1201 is used to acquire traffic information at each intersection node.

[0136] The traffic environment information determination module 1202 is used to determine traffic environment information based on the traffic information of each intersection node and generate vehicle driving route prediction information.

[0137] The vehicle guidance route planning module 1203 is used to plan vehicle guidance routes based on driving route prediction information and traffic environment information.

[0138] Traffic light adjustment module 1204 is used to adjust the traffic lights at each intersection node based on the vehicle guidance route.

[0139] In the application, the traffic information acquisition module 1201 acquires traffic information of each intersection node, and then the traffic environment information determination module 1202 determines the traffic environment information based on the traffic information of each intersection node and generates vehicle driving route prediction information. Then, the vehicle guidance route planning module 1203 plans the vehicle guidance route based on the driving route prediction information and the traffic environment information. Finally, the traffic light adjustment module 1204 adjusts the traffic lights of each intersection node based on the vehicle guidance route to guide the driving of vehicles at each intersection node, realize vehicle diversion, and alleviate traffic congestion.

[0140] Each module in the aforementioned traffic control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0141] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 13As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a traffic control method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0142] Those skilled in the art will understand that Figure 13 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0143] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0144] Obtain traffic information from each intersection node, including vehicle location information.

[0145] Traffic environment information is determined based on traffic information at each intersection node, and vehicle route prediction information is generated. The traffic environment information includes congested intersection nodes.

[0146] Vehicle guidance routes are planned based on driving route prediction information and traffic environment information.

[0147] The traffic lights at each intersection are adjusted based on the vehicle guidance route.

[0148] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0149] Obtain traffic information from each intersection node, including vehicle location information.

[0150] Traffic environment information is determined based on traffic information at each intersection node, and vehicle route prediction information is generated. The traffic environment information includes congested intersection nodes.

[0151] Vehicle guidance routes are planned based on driving route prediction information and traffic environment information.

[0152] The traffic lights at each intersection are adjusted based on the vehicle guidance route.

[0153] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0154] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0155] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0156] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A traffic control method characterized by, The method includes: Obtain traffic information at each intersection node, wherein the traffic information includes vehicle location information; Traffic environment information is determined based on the traffic information of each intersection node, and vehicle travel route prediction information is generated, wherein the traffic environment information includes congested intersection nodes. Plan vehicle guidance routes based on the predicted driving routes and the traffic environment information; The traffic lights at each intersection node are adjusted based on the vehicle guidance route. The step of determining traffic environment information based on the traffic information at each intersection node and generating vehicle route prediction information includes: Based on the traffic information, obtain each primary intersection node that is congested; based on each primary intersection node, obtain each secondary intersection node; the distance between the secondary intersection node and the primary intersection node is less than a preset distance threshold; the preset distance threshold is the distance between the primary intersection node and the intersection node that is adjacent to the primary intersection node, connected by a road, and farthest from it. Obtain all tertiary intersection nodes other than the aforementioned primary intersection nodes and secondary intersection nodes; wherein, higher-level intersection nodes cover lower-level intersection nodes, the level of primary intersection nodes is higher than that of secondary and tertiary intersection nodes, and the level of secondary intersection nodes is higher than that of tertiary intersection nodes. The traffic environment information is determined based on each of the primary intersection nodes, each of the secondary intersection nodes, and each of the tertiary intersection nodes.

2. The traffic control method according to claim 1, characterized by, The acquisition of traffic information at each intersection node includes: Obtain the image information uploaded by each of the aforementioned intersection nodes; The traffic information of each intersection node is obtained based on the image information.

3. The traffic control method according to claim 1, characterized in that, The step of obtaining each secondary intersection node based on each of the primary intersection nodes includes: Each intersection node that is adjacent to the first-level intersection node and has road connectivity is identified as the second-level intersection node.

4. The traffic control method according to claim 3, characterized in that, The step of obtaining each secondary intersection node based on each of the primary intersection nodes includes: If the road passing through the primary intersection node includes an urban arterial road, the target intersection node located on the urban arterial road is designated as a secondary intersection node, wherein the target intersection node is adjacent to the secondary intersection node located on the same urban arterial road.

5. The traffic control method according to claim 1, characterized in that, The generated vehicle route prediction information includes: The driving direction of each vehicle is determined based on the location information of each vehicle within the predetermined time period; The vehicle's travel route prediction information is determined based on the travel direction of each vehicle.

6. The traffic control method according to claim 1, characterized in that, The step of planning vehicle guidance routes based on the predicted driving route information and the traffic environment information includes: The alternative intersection nodes for each of the first-level intersection nodes, the second-level intersection nodes, the third-level intersection nodes, and the driving route prediction information are obtained, wherein the alternative intersection nodes include at least one of the second-level intersection nodes. The vehicle guidance route is planned based on each of the alternative intersection nodes and the driving route prediction information.

7. The traffic control method according to claim 1, characterized in that, The step of obtaining the primary intersection nodes with congestion based on the traffic information includes: Obtain the distance traveled by vehicles at each intersection node during a single traffic cycle; The intersection nodes where the travel distance of the first n vehicles in a single traffic cycle is less than a preset threshold are identified as the first-level intersection nodes.

8. The traffic control method according to claim 1, characterized in that, The method further includes: If a pedestrian is detected to maintain an abnormal posture on a road where vehicles are traveling for a period of time that reaches a first preset time, an alarm message will be sent. If a pedestrian is detected to maintain an abnormal posture on a road for a period of time that reaches a second preset time, an emergency medical information will be sent.

9. The traffic control method according to claim 8, characterized in that, The method further includes: Pedestrians who maintain an abnormal posture for a period of time up to the second preset time are identified as accident victims; Collect facial images of the accident victims and obtain and send their identity information.

10. The traffic control method according to claim 1, characterized in that, The method further includes: Obtain driving reference data, wherein the driving reference data includes at least one of historical vehicle driving data and driving planning data uploaded by associated terminals; The driving route prediction information is adjusted based on the driving reference data.

11. A traffic control device, characterized in that, The device includes: The traffic information acquisition module is used to acquire traffic information at each intersection node; The traffic environment information determination module is used to determine traffic environment information based on the traffic information of each intersection node and generate vehicle travel route prediction information. The vehicle guidance route planning module is used to plan vehicle guidance routes based on the driving route prediction information and the traffic environment information. The traffic light adjustment module is used to adjust the traffic lights at each intersection node based on the vehicle guidance route. The traffic environment information determination module is further configured to: obtain each primary intersection node experiencing congestion based on the traffic information; obtain each secondary intersection node based on each primary intersection node; the distance between each secondary intersection node and each primary intersection node is less than a preset distance threshold; the preset distance threshold is the distance between a primary intersection node and the intersection node adjacent to and connected to the primary intersection node and the furthest from it; obtain each tertiary intersection node other than each primary and secondary intersection node; wherein higher-level intersection nodes cover lower-level intersection nodes, the level of primary intersection nodes is higher than that of secondary and tertiary intersection nodes, and the level of secondary intersection nodes is higher than that of tertiary intersection nodes; and determine the traffic environment information based on each primary, secondary, and tertiary intersection node.

12. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 10.

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