Traffic information management methods, systems and equipment

Abstract

This application discloses a traffic information management method, system, and device, comprising: obtaining traffic flow prediction data for preset key nodes within a target area based on historical traffic data; determining whether abnormal nodes exist based on the error between real-time traffic data and traffic flow prediction data of the key nodes; when abnormal nodes exist, obtaining regional correlation data corresponding to the abnormal nodes; obtaining at least one abnormal region based on the regional correlation data; obtaining target vehicles and their travel plans based on the at least one abnormal region; and sending route change suggestions to at least some of the target vehicles based on their travel plans. This solves the problem in existing technologies where the algorithms relied upon have a certain lag, often leading to actual traffic jams lasting much longer than predicted.

Description

Technical Field

[0001] This application relates to the field of Internet big data technology, and in particular to traffic information management methods, systems and equipment. Background Technology

[0002] Traffic data management technology generally refers to systems that improve traffic management efficiency by collecting, storing, analyzing, and optimizing traffic flow data. In the past, traffic management mainly relied on manual patrols and simple traffic signal control methods. However, with the rapid development of information technology, especially the application of big data, cloud computing, and the Internet of Things (IoT), traffic management is gradually transforming towards intelligence and automation. Through real-time data acquisition systems, such as traffic cameras, sensors, and GPS positioning technology, management departments can monitor traffic flow in real time and predict traffic trends through data analysis, thereby making corresponding adjustments.

[0003] Currently, most navigation software on the market uses acquired traffic information to assist users in route planning, such as allowing them to choose the route with the shortest travel time or the shortest distance. This function has high prediction accuracy when traffic is normal or slightly congested. However, when encountering severe traffic congestion, the algorithms that existing methods rely on have a certain lag, which often leads to the actual traffic jam time being much longer than the predicted time. At the same time, incorrect predictions also cause more users to choose congested routes, further aggravating the congestion situation. Summary of the Invention

[0004] This invention provides a traffic information management method, system, and device, and offers a traffic information processing method to address at least the problem that in the prior art, most navigation software on the market uses acquired traffic information to assist users in route planning, such as selecting the route with the shortest travel time or the shortest distance. This function has high prediction accuracy when traffic is normal or slightly congested. However, when encountering severe traffic congestion, due to the certain lag in the algorithms relied upon by existing methods, the actual traffic jam time often exceeds the predicted time. At the same time, incorrect predictions also lead more users to choose congested routes, further exacerbating the congestion situation.

[0005] Traffic information management methods include:

[0006] Based on historical traffic data within the target area, obtain traffic flow prediction data for preset key nodes within the target area;

[0007] Based on the error between the real-time traffic data and the traffic prediction data of key nodes, determine whether there are abnormal nodes. Abnormal nodes are considered key nodes.

[0008] When abnormal nodes exist, obtain the regional correlation data corresponding to the abnormal nodes based on the abnormal nodes;

[0009] Based on the regional correlation data, at least one abnormal region is identified, and the abnormal region belongs to the target range.

[0010] Based on at least one abnormal region, obtain the target vehicle and the target vehicle's driving plan, wherein the target vehicle is configured to be a vehicle whose driving plan passes through at least one abnormal region.

[0011] Based on the target vehicles' travel plans, route change suggestions are sent to at least some of the target vehicles.

[0012] Optionally, based on historical traffic data within the target area, traffic flow prediction data for preset key nodes within the target area can be obtained, including:

[0013] Based on historical traffic data within the target area, the historical traffic data is input into a pre-trained spatiotemporal fusion model to obtain traffic flow prediction data for preset key nodes within the target area.

[0014] Optionally, based on the error between real-time traffic data and traffic prediction data of key nodes, determine whether there are abnormal nodes, including:

[0015] Based on the transportation network within the target area, obtain the relationship graph of key nodes;

[0016] Based on the error between the real-time traffic data and the traffic prediction data of key nodes, the error rate of each key node is obtained.

[0017] Based on the relationship graph and the error rate of each key node, determine whether there are key nodes with abnormal error rates;

[0018] If there is a critical node with an abnormal error rate, then that node is an abnormal node.

[0019] Optionally, when an abnormal node exists, the region correlation data corresponding to the abnormal node is obtained, including:

[0020] Based on the transportation network within the target area, obtain the relationship graph of key nodes;

[0021] Based on the abnormal nodes, obtain the correlation between each abnormal node;

[0022] When the correlation between any two abnormal nodes is greater than the preset correlation, all key nodes between the two abnormal nodes with a correlation greater than the preset correlation are set as abnormal nodes.

[0023] Based on the abnormal nodes, obtain the regional correlation data corresponding to the abnormal nodes.

[0024] Optionally, based on at least one abnormal area, the target vehicle and its driving plan are obtained, including:

[0025] Obtain navigation information for all vehicles, and based on the navigation information for all vehicles, identify the vehicles that are scheduled to pass through and / or have at least one abnormal area as their destination within a preset time as target vehicles.

[0026] Based on the target vehicle, obtain the target vehicle's driving plan.

[0027] Optionally, based on the target vehicle's travel plan, route change suggestions are sent to at least some of the target vehicles, including:

[0028] Based on the target vehicle's travel plan, route change suggestions are sent to vehicles within the target vehicle that are scheduled to pass through at least one abnormal area within a preset time but do not end at any of the abnormal areas.

[0029] Optionally, based on the target vehicle's travel plan, route change suggestions are sent to at least some of the target vehicles, including:

[0030] Based on the target vehicle's travel plan, the abnormal area that the target vehicle passes through within a preset time is identified as the first abnormal area.

[0031] After marking the roads involved in the first abnormal area as impassable in the navigation map, route change suggestions for the target vehicle are obtained based on the real-time location and destination of the target vehicle.

[0032] Based on the route change recommendations for all target vehicles, send route change recommendations to at least some of the target vehicles.

[0033] Optionally, based on the route change suggestions for all target vehicles, route change suggestions are sent to at least a portion of the target vehicles, including:

[0034] Based on the route change suggestions for all target vehicles, the target vehicles are sorted according to the changes in travel distance and time caused by the route change suggestions and the load of the new routes to obtain the target vehicle sequence.

[0035] Based on the degree of abnormality of the abnormal nodes, the proportion of target vehicles that need to be rerouted is obtained;

[0036] Based on the target vehicle sequence and the proportion of target vehicles requiring route changes, route change suggestions are sent to at least a portion of all target vehicles.

[0037] On the other hand, traffic information management systems include traffic monitoring platforms and route planning platforms;

[0038] The traffic monitoring platform is configured as follows:

[0039] Based on historical traffic data within the target area, obtain traffic flow prediction data for preset key nodes within the target area;

[0040] Based on the error between the real-time traffic data and the traffic prediction data of key nodes, determine whether there are abnormal nodes. Abnormal nodes are considered key nodes.

[0041] When abnormal nodes exist, obtain the regional correlation data corresponding to the abnormal nodes based on the abnormal nodes;

[0042] Based on the regional correlation data, at least one abnormal region is identified, and the abnormal region belongs to the target range.

[0043] Based on at least one abnormal region, obtain the target vehicle and the target vehicle's driving plan, wherein the target vehicle is configured to be a vehicle whose driving plan passes through at least one abnormal region.

[0044] The route planning platform is configured as follows:

[0045] Based on the target vehicles' travel plans, route change suggestions are sent to at least some of the target vehicles.

[0046] On the other hand, a computer device includes a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program to implement the above-described method.

[0047] On the other hand, a computer storage medium storing a computer program, wherein a processor executes the computer program to implement the above-described method.

[0048] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0049] This invention discloses a traffic information management method, system, and device, comprising: obtaining traffic flow prediction data for preset key nodes within a target range based on historical traffic data within the target range; determining whether there are abnormal nodes based on the error between real-time traffic data and traffic flow prediction data of the key nodes, wherein the abnormal nodes are key nodes; when abnormal nodes exist, obtaining regional correlation data corresponding to the abnormal nodes; obtaining at least one abnormal region based on the regional correlation data, wherein the abnormal region belongs to the target range; obtaining target vehicles and their travel plans based on the at least one abnormal region, wherein the target vehicles are configured as vehicles whose travel plans pass through at least one abnormal region; and sending route change suggestions to at least some of the target vehicles based on their travel plans. This approach addresses the current technology where most navigation software on the market relies on acquired traffic information to assist users in route planning, such as allowing them to choose the route with the shortest travel time or distance. While this function has high prediction accuracy under normal traffic conditions or with minor congestion, it often leads to significantly longer actual traffic jams due to the inherent lag in the algorithms used in existing methods. Furthermore, incorrect predictions can cause more users to choose congested routes, further exacerbating the congestion problem. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0051] Figure 1 This is a flowchart illustrating traffic information management methods.

[0052] Figure 2 This is a schematic diagram of a computer device.

[0053] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0054] To enable those skilled in the art to better understand the present disclosure, the technical solutions of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present disclosure, and not all embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present disclosure.

[0055] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0056] Example 1

[0057] like Figure 1 As shown, a traffic information management method includes:

[0058] S1. Based on historical traffic data within the target area, obtain traffic flow prediction data for preset key nodes within the target area.

[0059] This step allows us to obtain traffic prediction data for all key nodes within the target area, in order to prepare for subsequent information analysis and management.

[0060] Optionally, the target scope can be a city, administrative district, street, etc., and key nodes can be identified within the target scope. Generally, key nodes are set at intersections, large transportation hubs, or locations with relatively busy traffic. Historical traffic data is collected through traffic monitoring systems, sensors, mobile applications, or traffic management centers. Historical traffic data includes at least flow data and may also include data such as weather.

[0061] Optionally, machine learning or deep learning algorithms can be used to train historical data to generate a traffic prediction model, and the traffic prediction data of preset key nodes within the target range can be obtained through the traffic prediction model.

[0062] S2. Based on the error between the real-time traffic data and the traffic prediction data of key nodes, determine whether there are abnormal nodes.

[0063] Specifically, the abnormal node is a critical node.

[0064] This step helps identify key nodes with abnormal traffic flow, enabling timely detection and early warning of traffic congestion or abnormal traffic flow.

[0065] Optionally, traffic flow data of key nodes can be acquired in real time through traffic monitoring systems, sensors, etc. The error between the real-time flow data and the predicted flow data of key nodes can be compared. Then, by setting error conditions for abnormal nodes, it can be determined whether there are abnormal nodes that meet the conditions. The error conditions for abnormal nodes can be adjusted according to historical data and actual needs.

[0066] S3. When there are abnormal nodes, obtain the regional correlation data corresponding to the abnormal nodes.

[0067] This step allows us to obtain regional traffic data related to the detected abnormal nodes.

[0068] Optionally, the regional correlation data is configured to include the area of ​​influence corresponding to each key node and the degree of influence of the key node on that area. The area of ​​influence can be roads or road sections within a certain range around the key node.

[0069] Optionally, regional correlation data can be manually set and updated in real time by the user based on expert methods or other analysis methods.

[0070] Optionally, the regional correlation data between the target area and the key node can be obtained by analyzing the proportion of vehicles whose destination is the target area that pass through the key node.

[0071] S4. Based on the regional correlation data, identify at least one abnormal region.

[0072] Optionally, the abnormal area belongs to the target range.

[0073] This step allows for the identification of at least one region affected by an abnormal node, based on regional correlation data.

[0074] Optionally, based on the regional correlation data, at least one abnormal region with a correlation degree higher than a preset threshold with the abnormal node can be obtained.

[0075] S5. Based on at least one abnormal area, obtain the target vehicle and its driving plan.

[0076] The target vehicle is configured to travel through at least one abnormal area.

[0077] This step allows us to identify target vehicles planning to travel to abnormal areas and obtain their travel plans.

[0078] Optionally, by acquiring data from navigation software or navigation platforms, it is possible to identify the target vehicle that is planned to travel to the abnormal area, and also to determine the destination of the target vehicle.

[0079] S6. Based on the target vehicles' travel plans, send route change suggestions to at least some of the target vehicles.

[0080] This step allows for route change suggestions to potentially affected target vehicles, helping them avoid abnormal areas.

[0081] Optionally, route change suggestions may be sent to at least some of the target vehicles, depending on the destination and the magnitude of the impact of the route change.

[0082] This solution is applicable to navigation schemes with a certain user base, or route assignment platforms that manage a certain number of vehicles. The above method addresses at least the problem that most existing navigation software on the market uses acquired traffic information to assist users in route planning, such as allowing them to choose the route with the shortest travel time or the shortest distance. This function has high prediction accuracy when traffic is normal or slightly congested. However, when encountering severe traffic congestion, due to the lag in the algorithms relied upon by existing methods, the actual traffic jam time often exceeds the predicted time. Furthermore, incorrect predictions can lead more users to choose congested routes, further exacerbating the congestion problem.

[0083] Example 2

[0084] This embodiment, based on Embodiment 1, provides a traffic information management method, including:

[0085] S1. Based on historical traffic data within the target area, obtain traffic flow prediction data for preset key nodes within the target area.

[0086] Optionally, based on historical traffic data within the target area, traffic flow prediction data for preset key nodes within the target area can be obtained, including:

[0087] Based on historical traffic data within the target area, the historical traffic data is input into a pre-trained spatiotemporal fusion model to obtain traffic flow prediction data for preset key nodes within the target area.

[0088] Specifically, one can choose to use a combination of GNN and temporal models. Common architectures are Spatio-Temporal Graph Convolutional Networks (ST-GCN) or Spatio-Temporal Attention Networks (STAN).

[0089] Optional spatiotemporal fusion models include:

[0090] Configure the input layer to input historical traffic flow data, including node traffic flow, time information (such as hour, weekday / holiday, etc.), weather information, etc.

[0091] Configure the spatiotemporal fusion layer as follows:

[0092] Use GNN to model the spatial dependencies of the transportation network;

[0093] Use LSTM, GRU, or Transformer to model the temporal dependencies of time series;

[0094] Configure the output layer to output future traffic prediction data for each key node.

[0095] S2. Based on the error between the real-time traffic data and the traffic prediction data of key nodes, determine whether there are abnormal nodes.

[0096] The abnormal node is a critical node;

[0097] Optionally, based on the error between real-time traffic data and traffic prediction data of key nodes, determine whether there are abnormal nodes, including:

[0098] Based on the transportation network within the target area, obtain the relationship graph of key nodes;

[0099] Based on the error between the real-time traffic data and the traffic prediction data of key nodes, the error rate of each key node is obtained.

[0100] Based on the relationship graph and the error rate of each key node, determine whether there are key nodes with abnormal error rates;

[0101] If there is a critical node with an abnormal error rate, then that node is an abnormal node.

[0102] Optionally, the key node relationship graph can be obtained from road network data, in which each key node establishes a connection with its adjacent key nodes in the relationship graph.

[0103] Optionally, a critical node with an abnormal error rate refers to a critical node whose error rate significantly exceeds the error rates of at least some of the critical nodes associated with it. This significant difference can be that the critical node's error rate exceeds the average of the error rates of at least some of the critical nodes associated with it by a certain proportion. The critical nodes at least partially associated with it can be the remaining critical nodes after removing the critical nodes with the highest and lowest error rates from the critical nodes associated with it.

[0104] Specifically, if the error rate of critical node A is 25%, and the associated nodes of critical node A include critical nodes B, C, D, E, and F, with critical node B having an error rate of 20%, critical node C having an error rate of 12%, critical node D having an error rate of 5%, critical node E having an error rate of 8%, and critical node F having an error rate of 18%, the criterion for judging an abnormal error rate is that the error rate of a critical node exceeds 130% of the average error rate of at least some of the critical nodes associated with it. The average error rate of critical nodes B, C, D, E, and F can be calculated to be 12.6%. Since the error rate of critical node A (25%) is greater than the average error rate of 12.6% multiplied by 130%, it means that the error rate of critical node A exceeds 130% of the average error rate of all critical nodes associated with it, making critical node A an abnormal node.

[0105] S3. When there are abnormal nodes, obtain the regional correlation data corresponding to the abnormal nodes.

[0106] Optionally, when an abnormal node exists, the region correlation data corresponding to the abnormal node is obtained, including:

[0107] Based on the transportation network within the target area, obtain the relationship graph of key nodes;

[0108] Based on the abnormal nodes, obtain the correlation between each abnormal node;

[0109] When the correlation between any two abnormal nodes is greater than the preset correlation, all key nodes between the two abnormal nodes with a correlation greater than the preset correlation are set as abnormal nodes.

[0110] Based on the abnormal nodes, obtain the regional correlation data corresponding to the abnormal nodes.

[0111] Optionally, regional correlation data can be manually set and updated in real time by the user based on expert methods or other analysis methods.

[0112] Optionally, the regional correlation data between the target area and the key nodes can be obtained by analyzing the proportion of vehicles whose destination is the target area that pass through the key nodes.

[0113] S4. Based on the regional correlation data, identify at least one abnormal region.

[0114] Optionally, the abnormal area belongs to the target range.

[0115] S5. Based on at least one abnormal area, obtain the target vehicle and its driving plan.

[0116] The target vehicle is configured to travel through at least one abnormal area.

[0117] Optionally, based on at least one abnormal area, the target vehicle and its driving plan are obtained, including:

[0118] Obtain navigation information for all vehicles, and based on the navigation information for all vehicles, identify the vehicles that are scheduled to pass through and / or have at least one abnormal area as their destination within a preset time as target vehicles.

[0119] Based on the target vehicle, obtain the target vehicle's driving plan.

[0120] S6. Based on the target vehicles' travel plans, send route change suggestions to at least some of the target vehicles.

[0121] Optionally, based on the target vehicle's travel plan, route change suggestions are sent to at least some of the target vehicles, including:

[0122] Based on the target vehicle's travel plan, route change suggestions are sent to vehicles within the target vehicle that are scheduled to pass through at least one abnormal area within a preset time but do not end at any of the abnormal areas.

[0123] When making route change suggestions, if the destination of the target vehicle is an abnormal area, the target vehicle cannot avoid this abnormal area under any circumstances, so there is no need to make a route change.

[0124] Optionally, the preset time can be 10 minutes, 30 minutes, or one hour in the future. The specific selection can be preset by the user or determined based on the degree and / or type of the anomaly. Generally, the higher the degree of anomaly, the longer the preset time. Anomaly types can include traffic lights not working, traffic accidents, etc. The more severe the anomaly type, the longer the preset time. The degree of anomaly is configured to be obtained based on the difference between the traffic of all critical nodes in the anomaly area and the predicted traffic of these critical nodes.

[0125] Optionally, based on the target vehicle's travel plan, route change suggestions are sent to at least some of the target vehicles, including:

[0126] Based on the target vehicle's travel plan, route change suggestions are prioritized for vehicles that are scheduled to pass through more abnormal areas within a preset time.

[0127] Optionally, based on the target vehicle's travel plan, route change suggestions are sent to at least some of the target vehicles, including:

[0128] Based on the target vehicle's travel plan, the abnormal area that the target vehicle passes through within a preset time is identified as the first abnormal area.

[0129] After marking the roads involved in the first abnormal area as impassable in the navigation map, route change suggestions for the target vehicle are obtained based on the real-time location and destination of the target vehicle.

[0130] Based on the route change recommendations for all target vehicles, send route change recommendations to at least some of the target vehicles.

[0131] Optionally, based on the target vehicle's travel plan, route change suggestions are sent to at least some of the target vehicles, including:

[0132] Based on the target vehicle's travel plan, the abnormal area that the target vehicle passes through within a preset time is identified as the first abnormal area.

[0133] After marking the roads involved in the first abnormal area as impassable in the navigation map, corresponding prompt words are generated;

[0134] Based on the prompts, the target vehicle's real-time location, and its destination, a generative artificial intelligence model is used to obtain route change suggestions for the target vehicle.

[0135] Based on the route change recommendations for all target vehicles, send route change recommendations to at least some of the target vehicles.

[0136] Optionally, based on the route change suggestions for all target vehicles, route change suggestions are sent to at least a portion of the target vehicles, including:

[0137] Based on the route change suggestions for all target vehicles, the target vehicles are sorted according to the changes in travel distance and time caused by the route change suggestions and the load of the new routes to obtain the target vehicle sequence.

[0138] Based on the degree of abnormality of the abnormal nodes, the proportion of target vehicles that need to be rerouted is obtained;

[0139] Based on the target vehicle sequence and the proportion of target vehicles requiring route changes, route change suggestions are sent to at least a portion of all target vehicles.

[0140] Specifically, based on the route change suggestions for all target vehicles, and considering the resulting changes in travel distance, time, and load conditions on the new routes, all target vehicles are sorted to obtain a target vehicle sequence, including:

[0141] according to P = a * L + b * T + c * F Obtain the target vehicle weights, where, P The target vehicle weight value, a Factors affecting changes in travel distance. b The time-varying influencing factor, cThe load factors affecting the new route, L This represents the change in travel distance. T Values ​​that change over time. F This represents the load condition value for the new route;

[0142] All target vehicles are sorted according to their weights to obtain a target vehicle sequence;

[0143] Based on the degree of abnormality of the abnormal nodes, the proportion of target vehicles that need to be rerouted is obtained;

[0144] Based on the target vehicle sequence and the proportion of target vehicles requiring route changes, route change suggestions are sent to at least a portion of all target vehicles.

[0145] Optionally, the proportion of target vehicles requiring route changes is configured based on the degree of abnormality of the abnormal node. The higher the degree of abnormality of the abnormal node, the higher the proportion of target vehicles requiring route changes.

[0146] The above method addresses at least one key issue: existing navigation software largely relies on acquired traffic information to assist users in route planning, such as offering options for the shortest route or shortest distance. While this function provides high accuracy under normal or mild traffic conditions, it suffers from significant delays in severe congestion. The algorithms used in these methods have inherent lag, often resulting in actual traffic jams lasting much longer than predicted. This is because current navigation time predictions depend on the travel times of vehicles already passing through congested areas and those currently in congestion. This prediction doesn't account for the increased congestion caused by more vehicles entering the congested area. Users, seeing the predicted congestion time as relatively short, often don't proactively change their routes, leading to further congestion and longer delays once they enter the congested area. This proposed solution, however, proactively pushes route change suggestions upon detecting congestion, effectively resolving this problem.

[0147] Example 3

[0148] This embodiment discloses a traffic information management system, including a traffic monitoring platform and a route planning platform;

[0149] The traffic monitoring platform is configured as follows:

[0150] Based on historical traffic data within the target area, obtain traffic flow prediction data for preset key nodes within the target area;

[0151] Based on the error between the real-time traffic data and the traffic prediction data of key nodes, determine whether there are abnormal nodes. Abnormal nodes are considered key nodes.

[0152] When abnormal nodes exist, obtain the regional correlation data corresponding to the abnormal nodes based on the abnormal nodes;

[0153] Based on the regional correlation data, at least one abnormal region is identified, and the abnormal region belongs to the target range.

[0154] Based on at least one abnormal region, obtain the target vehicle and the target vehicle's driving plan, wherein the target vehicle is configured to be a vehicle whose driving plan passes through at least one abnormal region.

[0155] The route planning platform is configured as follows:

[0156] Based on the target vehicles' travel plans, route change suggestions are sent to at least some of the target vehicles.

[0157] Optionally, the route planning platform is configured to store the driving plans for all target vehicles.

[0158] Optionally, based on historical traffic data within the target area, traffic flow prediction data for preset key nodes within the target area can be obtained, including:

[0159] Based on historical traffic data within the target area, the historical traffic data is input into a pre-trained spatiotemporal fusion model to obtain traffic flow prediction data for preset key nodes within the target area.

[0160] Optionally, based on the error between real-time traffic data and traffic prediction data of key nodes, determine whether there are abnormal nodes, including:

[0161] Based on the transportation network within the target area, obtain the relationship graph of key nodes;

[0162] Based on the error between the real-time traffic data and the traffic prediction data of key nodes, the error rate of each key node is obtained.

[0163] Based on the relationship graph and the error rate of each key node, determine whether there are key nodes with abnormal error rates;

[0164] If there is a critical node with an abnormal error rate, then that node is an abnormal node.

[0165] Optionally, when an abnormal node exists, the region correlation data corresponding to the abnormal node is obtained, including:

[0166] Based on the transportation network within the target area, obtain the relationship graph of key nodes;

[0167] Based on the abnormal nodes, obtain the correlation between each abnormal node;

[0168] When the correlation between any two abnormal nodes is greater than the preset correlation, all key nodes between the two abnormal nodes with a correlation greater than the preset correlation are set as abnormal nodes.

[0169] Based on the abnormal nodes, obtain the regional correlation data corresponding to the abnormal nodes.

[0170] Optionally, based on at least one abnormal area, the target vehicle and its driving plan are obtained, including:

[0171] Obtain navigation information for all vehicles, and based on the navigation information for all vehicles, identify the vehicles that are scheduled to pass through and / or have at least one abnormal area as their destination within a preset time as target vehicles.

[0172] Based on the target vehicle, obtain the target vehicle's driving plan.

[0173] Optionally, based on the target vehicle's travel plan, route change suggestions are sent to at least some of the target vehicles, including:

[0174] Based on the target vehicle's travel plan, route change suggestions are sent to vehicles within the target vehicle that are scheduled to pass through at least one abnormal area within a preset time but do not end at any of the abnormal areas.

[0175] Optionally, based on the target vehicle's travel plan, route change suggestions are sent to at least some of the target vehicles, including:

[0176] Based on the target vehicle's travel plan, the abnormal area that the target vehicle passes through within a preset time is identified as the first abnormal area.

[0177] After marking the roads involved in the first abnormal area as impassable in the navigation map, route change suggestions for the target vehicle are obtained based on the real-time location and destination of the target vehicle.

[0178] Based on the route change recommendations for all target vehicles, send route change recommendations to at least some of the target vehicles.

[0179] Optionally, based on the route change suggestions for all target vehicles, route change suggestions are sent to at least a portion of the target vehicles, including:

[0180] Based on the route change suggestions for all target vehicles, the target vehicles are sorted according to the changes in travel distance and time caused by the route change suggestions and the load of the new routes to obtain the target vehicle sequence.

[0181] Based on the degree of abnormality of the abnormal nodes, the proportion of target vehicles that need to be rerouted is obtained;

[0182] Based on the target vehicle sequence and the proportion of target vehicles requiring route changes, route change suggestions are sent to at least a portion of all target vehicles.

[0183] Example 4

[0184] This embodiment provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the methods described above.

[0185] Optional, such as Figure 2 As shown, Figure 2 This is a schematic diagram of a computer device, which is an electronic device. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to enable communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface or a wireless interface. The network interface 103 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.

[0186] Those skilled in the art will understand that Figure 2 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0187] like Figure 2 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and an application program for implementing traffic information management methods.

[0188] exist Figure 2 In the computer device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device, and the electronic device can call the application program stored in the memory 105 to implement the traffic information management method through the processor 101 to implement the above method.

[0189] Example 5

[0190] This embodiment provides a computer storage medium on which a computer program is stored. A processor executes the computer program to implement any of the methods described above.

[0191] In some embodiments, the computer storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0192] In the above embodiments of this disclosure, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0193] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0194] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0195] Furthermore, the functional units in the various embodiments of this disclosure 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. The integrated unit can be implemented in hardware or as a software functional unit.

[0196] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0197] The above are merely preferred embodiments of this disclosure. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this disclosure, and these improvements and modifications should also be considered within the scope of protection of this disclosure.

Claims

1. A traffic information management method characterized by, The method comprises the following steps: obtaining traffic prediction data of preset key nodes in a target range according to historical traffic data in the target range; judging whether there is an abnormal node according to the error between real-time traffic data of the key nodes and the traffic prediction data, wherein the abnormal node belongs to the key nodes; when there is an abnormal node, obtaining regional correlation degree data corresponding to the abnormal node according to the abnormal node; obtaining at least one abnormal region according to the regional correlation degree data, wherein the abnormal region belongs to the target range; obtaining a target vehicle and a driving plan of the target vehicle according to the at least one abnormal region, wherein the target vehicle is configured as a vehicle whose driving plan passes through at least one abnormal region; obtaining a first abnormal region through which the target vehicle passes within a preset time according to the driving plan of the target vehicle; after marking a road related to the first abnormal region as impassable in a navigation map, obtaining a route change suggestion of the target vehicle according to a real-time position and a terminal point of the target vehicle; sorting all target vehicles according to a travel distance change, a time change and a load condition of a new route caused by the route change suggestion to obtain a target vehicle sequence according to all route change suggestions of the target vehicles; obtaining a proportion of target vehicles that need to change routes according to an abnormal degree of the abnormal node; sending a route change suggestion to at least part of the target vehicles according to the target vehicle sequence and the proportion of the target vehicles that need to change routes.

2. The traffic information management method according to claim 1, characterized by, The method comprises the following steps: obtaining traffic prediction data of preset key nodes in a target range according to historical traffic data in the target range; 3. The traffic information management method according to claim 1, characterized by, obtaining traffic prediction data of preset key nodes in a target range according to historical traffic data in the target range; The method comprises the following steps: obtaining a relationship graph of the key nodes according to a traffic network in the target range; obtaining an error rate of each key node according to the error between real-time traffic data of the key nodes and the traffic prediction data; judging whether there is a key node with an abnormal error rate according to the relationship graph and the error rate of each key node; 4. The traffic information management method according to claim 1, characterized by, if there is a key node with an abnormal error rate, the node is an abnormal node. The method comprises the following steps: obtaining a relationship graph of the key nodes according to a traffic network in the target range; obtaining a correlation degree between each abnormal node according to the abnormal node; when the correlation degree between any two abnormal nodes is greater than a preset correlation degree, setting all key nodes between the two abnormal nodes with the correlation degree greater than the preset correlation degree as abnormal nodes; 5. The traffic information management method according to claim 1, characterized by, obtaining regional correlation degree data corresponding to the abnormal node according to the abnormal node. The method comprises the following steps: Obtaining navigation information of all vehicles, and obtaining target vehicles that are planned to pass through at least one of the abnormal areas and / or end at the abnormal areas within a preset time according to the navigation information of all vehicles; Obtaining a driving plan of the target vehicles according to the target vehicles.

6. The traffic information management method according to claim 1, characterized by, The driving plan of the target vehicles includes: Sending a route change suggestion to at least part of the target vehicles according to the driving plan of the target vehicles.

7. Traffic information management system, characterized in that The driving plan of the target vehicles includes: The traffic monitoring platform and the route planning platform are included; The traffic monitoring platform is configured to: Obtaining traffic prediction data of a preset key node in the target range according to historical traffic data in the target range; Determining whether there is an abnormal node according to an error between real-time traffic data of the key node and the traffic prediction data, the abnormal node belonging to the key node; When there is an abnormal node, obtaining area correlation data corresponding to the abnormal node according to the abnormal node; Obtaining at least one abnormal area according to the area correlation data, the abnormal area belonging to the target range; Obtaining target vehicles and driving plans of the target vehicles according to the at least one abnormal area, the target vehicles being configured to pass through at least one of the abnormal areas; The route planning platform is configured to: Obtaining a first abnormal area that is passed through by the target vehicles within a preset time according to the driving plans of the target vehicles; After marking a road related to the first abnormal area as impassable in a navigation map, obtaining a route change suggestion of the target vehicles according to real-time positions and endpoints of the target vehicles; According to the route change suggestions of all target vehicles, sorting all target vehicles according to travel distance changes, time changes and load conditions of new routes caused by the route change suggestions to obtain a target vehicle sequence; Obtaining a proportion of target vehicles that need to change routes according to an abnormal degree of the abnormal node; 8. A computer device, comprising: According to the target vehicle sequence and the proportion of the target vehicles that need to change routes, sending a route change suggestion to at least part of the target vehicles. The computer device includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the method in any one of claims 1-6.

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