A highway traffic prediction method and system based on cloud-edge

Abstract

The present invention relates to the field of traffic control technology, and discloses a highway traffic prediction method and system based on cloud-edge terminals, including: configuring data acquisition equipment for highways, building a full-area road perception network for highways; configuring edge computing nodes for highways at key hubs, obtaining lightweight data packets for highways; generating cloud storage data for highways based on lightweight data packets, analyzing traffic patterns for highways; identifying traffic flow change trends and abnormal traffic conditions for highways, setting up abnormal self-service processing mechanisms and intelligent auxiliary systems for highways; defining traffic management modes and user interaction mechanisms for highways, generating data sharing platforms for highways; combining full-area road perception networks, traffic flow change trends, and data sharing platforms, executing traffic prediction processing for highways, and obtaining prediction processing results. The present invention can improve the prediction efficiency and accuracy of highway traffic prediction systems.

Description

Technical Field

[0001] The present invention relates to a cloud-edge-end based highway traffic prediction method and system, belonging to the technical field of traffic control. Background Art

[0002] Highway traffic forecasting refers to the process of analyzing and predicting the traffic conditions of highways in the future by using various data, models and algorithms. Its main purpose is to better manage and optimize highway traffic flow and improve traffic efficiency. As an important national transportation infrastructure, the stability and security of the highway management system are directly related to the long-term operation of the national infrastructure. The cloud-edge system, as the core intelligent system for highways to perceive traffic conditions in real time and optimize traffic flow, has become a key factor in the upgrade of highway system management.

[0003] In the prior art, a Chinese invention patent with publication number CN117409575A discloses a highway traffic congestion prediction method based on artificial intelligence, and the classification number is G08G, which belongs to the field of highway road condition analysis technology. The method includes the following steps: S1. Divide the highway into sections and branches in advance, and deploy cameras and sensors on the highway; S2. The cloud server collects camera and sensor data from each section of the highway and constructs an artificial intelligence algorithm model; S3. The cloud server uses the artificial intelligence algorithm model to predict traffic congestion on each section of the highway, and pushes the prediction results to users, and corrects the artificial intelligence algorithm model according to the accuracy of the prediction results. This patent uses the artificial intelligence algorithm model to predict the road condition of the highway according to the section, recommend the road condition to relevant vehicle users, identify congestion predictions in a timely manner, and push recommended routes to users before congestion occurs, so as to minimize the impact of congestion and improve the overall traffic efficiency of the highway.

[0004] Traditional highway traffic prediction mainly collects and processes data from different highway monitoring points on a central server, and predicts and analyzes highway traffic flow based on the data analysis results. However, this method has limited computing resources and poor real-time performance, making it difficult to cope with the real-time processing requirements of large-scale data, which limits the prediction efficiency and accuracy of the highway traffic prediction system.

[0005] Therefore, there is an urgent need for a solution that can improve the prediction efficiency and accuracy of highway traffic prediction systems. Summary of the invention

[0006] The present invention provides a cloud-edge-based highway traffic prediction method and system, the main purpose of which is to improve the prediction efficiency and accuracy of the highway traffic prediction system.

[0007] To achieve the above objectives, the present invention provides a highway traffic prediction method based on cloud-edge-end, comprising:

[0008] Acquire a highway to be managed, configure data acquisition equipment for the highway, and build a full-area road perception network for the highway based on the data acquisition equipment;

[0009] Based on the full-area road perception network, identify key hubs of the expressway, configure edge computing nodes of the expressway at the key hubs, and obtain lightweight data packets of the expressway according to the edge computing nodes;

[0010] generating cloud storage data of the expressway according to the lightweight data packet, and analyzing the traffic pattern of the expressway based on the cloud storage data;

[0011] According to the traffic pattern, identifying the traffic flow change trend of the expressway, analyzing the abnormal traffic situation of the expressway based on the traffic flow change trend, and setting the abnormal self-service processing mechanism and intelligent assistance system of the expressway according to the abnormal traffic situation;

[0012] Based on the abnormal self-service processing mechanism and the intelligent assistance system, a traffic management mode of the expressway is defined, according to the traffic management mode, a user interaction mechanism of the expressway is set, and based on the traffic management mode and the user interaction mechanism, a data sharing platform of the expressway is constructed;

[0013] In combination with the full-area road perception network, the traffic flow change trend and the data sharing platform, traffic prediction processing of the expressway is performed to obtain a prediction processing result.

[0014] Optionally, the constructing of the full-area road perception network of the expressway based on the data acquisition device includes:

[0015] Based on the data acquisition device, identifying traffic participants on the highway;

[0016] Collecting the perception data of the traffic participants and setting a data processing unit for the perception data;

[0017] According to the data acquisition device and the data processing unit, an information transmission layer of the highway is set;

[0018] Based on the information transmission layer, identifying the transmission data value of the highway;

[0019] According to the value of the transmitted data, setting the application terminal of the highway;

[0020] The data acquisition device, the data processing unit, the information transmission layer and the application terminal are combined to construct a full-area road perception network for the expressway.

[0021] Optionally, the identifying key hubs of the expressway based on the full-area road perception network includes:

[0022] Based on the full-area road sensing network, collecting traffic flow data, vehicle driving trajectory data and road service facilities of the expressway;

[0023] According to the traffic flow data, the traffic flow of the expressway is collected, and based on the traffic flow, the congestion index of the expressway is calculated using the following formula:

[0024] ;

[0025] Among them, I represents the congestion index of the highway, Q represents the traffic flow of the highway, A represents the maximum number of vehicles that can pass through per unit time, L represents the length of the queue of vehicles on the highway under non-congestion conditions, and L represents the maximum queue length of vehicles on the highway under the most congested conditions. It indicates the speed of vehicles on the highway without congestion. It indicates the speed of vehicles on the highway in congestion;

[0026] identifying a busy section of the highway according to the traffic flow and the congestion index;

[0027] Analyzing the vehicle flow situation on the expressway based on the vehicle driving trajectory data;

[0028] According to the road service facilities, identifying the functional area location of the expressway;

[0029] The key hubs of the expressway are identified by combining the busy road sections, the vehicle flow conditions and the functional area locations.

[0030] Optionally, identifying the traffic flow change trend of the expressway according to the traffic mode includes:

[0031] collecting traffic data of the highway according to the traffic pattern;

[0032] Based on the traffic data, creating a time series graph of the highway;

[0033] According to the time series graph, identifying the vehicle type distribution and peak and valley time periods of the expressway;

[0034] Analyze the hotspot congestion areas of the highway based on the vehicle type distribution and the peak and valley time periods;

[0035] The historical traffic data of the hot spot congestion area is collected, and the traffic flow change trend of the expressway is identified based on the historical traffic data.

[0036] Optionally, setting the abnormal self-service processing mechanism of the expressway according to the abnormal traffic situation includes:

[0037] Identifying the event type of the abnormal traffic situation;

[0038] According to the event type, setting an alarm triggering mode for the abnormal traffic situation;

[0039] Based on the alarm triggering mode, identifying the development trend of the abnormal traffic situation;

[0040] According to the development trend, a linkage control network for the abnormal traffic situation is constructed;

[0041] Analyzing the urgency of the event type, and defining a remote response method and an automatic adjustment mechanism of the linkage control network according to the urgency and the event type;

[0042] In combination with the alarm triggering mode, the remote response method and the automatic adjustment mechanism, an abnormal self-service processing mechanism for the highway is set up.

[0043] Optionally, the intelligent assistance system of the expressway is set according to the abnormal traffic situation, including:

[0044] identifying an abnormal response capability of the expressway according to the abnormal traffic condition;

[0045] Based on the abnormal response capability, an intelligent lane guard booth is set up on the highway;

[0046] Locating vehicle entry and exit points of the expressway, and setting vehicle travel interference reminders for the expressway according to the vehicle entry and exit points;

[0047] Based on the intelligent lane guard booth and vehicle travel interference reminder, identify the abnormal lane of the expressway and set a spare lane for the abnormal lane;

[0048] Identifying a lane blocking device of the highway and defining a release and payment method for the lane blocking device;

[0049] In combination with the intelligent lane guard booth, the spare lane and the release and payment method, an intelligent auxiliary system for the expressway is set up.

[0050] Optionally, the traffic management mode of the expressway is defined based on the abnormal self-service processing mechanism and the intelligent assistance system, including:

[0051] Based on the abnormal self-service processing mechanism, identifying abnormal traffic conditions on the expressway;

[0052] Analyzing the causes of the abnormal traffic condition and setting the adaptive control mode of the expressway according to the causes;

[0053] Based on the abnormal traffic condition and the adaptive control method, setting an information interaction mechanism for the expressway;

[0054] identifying emergency response rules for the expressway according to the intelligent assistance system;

[0055] The traffic management mode of the expressway is defined by combining the emergency response rules, the adaptive control method and the information interaction mechanism.

[0056] Optionally, constructing the highway data sharing platform based on the traffic management mode and the user interaction mechanism includes:

[0057] Based on the traffic management mode, the integrated traffic data of the expressway is collected, and a data set library of the integrated traffic data is constructed;

[0058] Based on the user interaction mechanism, identifying the users who need the data collection library;

[0059] Analyze the data sensitivity of the data collection library, and set the data access scope and identity identification password of the required user according to the data sensitivity;

[0060] Based on the data access scope and the identity identification password, a data query entry and a data display interface of the data collection library are set;

[0061] The data query portal and the data display interface are combined to generate a data sharing platform for the expressway.

[0062] In order to solve the above problems, the present invention also provides a highway traffic prediction system based on cloud edge, the system comprising:

[0063] A road perception module, used to obtain a highway to be managed, configure data acquisition equipment for the highway, and build a full-area road perception network for the highway based on the data acquisition equipment;

[0064] An edge computing module, for identifying key hubs of the expressway based on the full-area road perception network, configuring edge computing nodes of the expressway at the key hubs, and acquiring lightweight data packets of the expressway according to the edge computing nodes;

[0065] A cloud data analysis module, used to generate cloud storage data of the expressway according to the lightweight data packet, and analyze the traffic pattern of the expressway based on the cloud storage data;

[0066] A traffic trend analysis module, used to identify the traffic flow change trend of the expressway according to the traffic mode, analyze the abnormal traffic situation of the expressway based on the traffic flow change trend, and set up an abnormal self-service processing mechanism and intelligent assistance system for the expressway according to the abnormal traffic situation;

[0067] A data sharing module, used to define a traffic management mode of the expressway based on the abnormal self-service processing mechanism and the intelligent assistance system, set a user interaction mechanism of the expressway according to the traffic management mode, and build a data sharing platform of the expressway based on the traffic management mode and the user interaction mechanism;

[0068] The traffic prediction module is used to combine the whole-area road perception network, the traffic flow change trend and the data sharing platform to perform traffic prediction processing on the expressway and obtain a prediction processing result.

[0069] Compared with the problems described in the background technology, the embodiment of the present invention can collect traffic data on the road in real time and improve the intelligence level of road traffic by constructing a full-area road perception network of the highway based on the data acquisition equipment; further, the embodiment of the present invention can configure the edge computing node of the highway at the key hub, and obtain the lightweight data packet of the highway according to the edge computing node, so that the data processing task can be carried out close to the data source, thereby reducing the delay in data transmission to the central server, reducing bandwidth consumption, and improving data processing efficiency; secondly, the embodiment of the present invention can help identify traffic patterns, predict traffic flow and optimize traffic management by generating cloud storage data of the highway according to the lightweight data packet; thirdly, the embodiment of the present invention can reduce the time of manual intervention and improve the processing speed by setting up the abnormal self-service processing mechanism of the highway according to the abnormal traffic conditions, so as to reduce the risk of secondary accidents and improve road safety, and at the same time, it can also reduce the workload of the traffic management center and reduce operating costs, and by setting up the intelligent auxiliary system of the highway, it can reduce manual intervention, improve travel efficiency, and improve the travel experience of drivers; in addition, the embodiment of the present invention can reduce the time of manual intervention and improve the travel efficiency by setting up the intelligent auxiliary system of the highway according to the abnormal self-service processing mechanism. The auxiliary processing mechanism and the intelligent auxiliary system define the traffic management mode of the expressway, set the user interaction mechanism of the expressway, can provide real-time traffic information and guidance, increase the driver's understanding of the current traffic conditions, improve information transparency and driver response speed, and achieve global optimization of the traffic management system; further, the embodiment of the present invention builds a data sharing platform for the expressway based on the traffic management mode and the user interaction mechanism, can exchange information through the data sharing platform, realize cross-departmental collaborative work, improve the overall efficiency of traffic management, and through integrated monitoring data, monitor road safety conditions in real time, quickly respond to traffic accidents and emergencies, and help decision makers make faster and more accurate decisions; finally, the embodiment of the present invention performs traffic prediction processing on the expressway by combining the full-area road perception network, the traffic flow change trend and the data sharing platform to obtain the prediction processing result, which can improve the input data quality of the traffic prediction model, thereby improving the accuracy of traffic prediction, and can also help the traffic management department to reasonably allocate traffic resources, realize intelligent management of expressways, and improve the management efficiency and accuracy of expressways, so as to enhance the country's governance capabilities in the field of transportation and promote economic growth. Therefore, the cloud-edge-based highway traffic prediction method and system provided by the embodiments of the present invention can improve the prediction efficiency and accuracy of the highway traffic prediction system. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of a flow chart of a cloud-edge-based highway traffic prediction method provided in one embodiment of the present invention;

[0071] Figure 2 A schematic diagram of a module for implementing the cloud-edge-based highway traffic prediction method provided in one embodiment of the present invention.

[0072] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0073] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0074] The embodiment of the present application provides a highway traffic prediction method based on the cloud edge. The execution subject of the highway traffic prediction method based on the cloud edge includes but is not limited to at least one of the electronic devices such as the server, the terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the highway traffic prediction method based on the cloud edge can be executed by software or hardware installed on the terminal device or the server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0075] Example 1

[0076] Reference Figure 1 FIG. 1 is a flow chart of a highway traffic prediction method based on cloud-edge terminal provided by an embodiment of the present invention. In this embodiment, the highway traffic prediction method based on cloud-edge terminal includes:

[0077] S1. Acquire a highway to be managed, configure data acquisition equipment for the highway, and build a full-area road perception network for the highway based on the data acquisition equipment.

[0078] The embodiment of the present invention can provide data support for subsequent cloud control management of highways by acquiring highways to be managed and configuring data collection equipment for the highways. The highways refer to roads specially designed for high-speed travel of various types of vehicles, such as buses, trucks, etc. The data collection equipment refers to tools and devices used to collect information related to highways, such as vehicle detectors, cameras, etc.

[0079] Optionally, the data collection equipment configuration of the highway can be implemented using sensors and vehicle detectors, such as road surface sensors.

[0080] Furthermore, the embodiment of the present invention can collect traffic data on the road in real time and improve the intelligence level of road traffic by constructing a full-area road perception network of the highway based on the data acquisition equipment. The full-area road perception network refers to a system that uses various technical means to comprehensively perceive and monitor road conditions in the entire area.

[0081] As an embodiment of the present invention, the full-area road perception network of the expressway is constructed based on the data acquisition device, including: based on the data acquisition device, identifying traffic participants on the expressway; collecting perception data of the traffic participants and setting a data processing unit for the perception data; setting an information transmission layer of the expressway based on the data acquisition device and the data processing unit; based on the information transmission layer, identifying the transmission data value of the expressway; setting an application terminal of the expressway based on the transmission data value; and combining the data acquisition device, the data processing unit, the information transmission layer and the application terminal to construct the full-area road perception network of the expressway.

[0082] Among them, the traffic participants refer to all entities using the highway, including drivers, passengers, pedestrians and the vehicles they drive or ride in; the perception data refers to the original data on the traffic conditions of the highway collected by various sensors, cameras and other data acquisition devices; the data processing unit refers to the software or hardware system that stores, analyzes and processes the collected data; the information transmission layer refers to the network layer responsible for transmitting the collected data and processing results between different systems and devices on the highway; the transmission data value refers to the role and importance of data in the operation and management of the highway, such as for traffic flow analysis, accident detection, etc.; the application terminal refers to the device with which the user directly interacts, such as the display screen of the monitoring center, the driver's vehicle system, etc.

[0083] Optionally, the data processing unit setting of the perception data can be implemented using an edge computing device. Based on the data acquisition device and the data processing unit, the information transmission layer setting of the highway can be obtained through wireless communication technology. According to the value of the transmission data, the application terminal setting of the highway can be implemented using a visualization tool, such as a display screen. Based on the information transmission layer, the transmission data value of the highway can be identified by determining the data usage.

[0084] S2. Based on the full-area road perception network, identify the key hubs of the expressway, configure edge computing nodes of the expressway at the key hubs, and obtain lightweight data packets of the expressway based on the edge computing nodes.

[0085] The embodiment of the present invention can identify the key hubs of the highway based on the full-area road perception network, and can select suitable locations to build an edge computing platform for the highway. The key hub refers to the central point of the highway located in an important geographical location, such as a connection point between cities, provincial boundaries, national borders, etc.

[0086] As an embodiment of the present invention, the identifying the key hubs of the expressway based on the full-area road sensing network includes: based on the full-area road sensing network, collecting traffic flow data, vehicle driving trajectory data and road service facilities of the expressway; collecting the traffic flow of the expressway according to the traffic flow data, and calculating the congestion index of the expressway based on the traffic flow; identifying the busy sections of the expressway according to the traffic flow and the congestion index; analyzing the vehicle flow conditions of the expressway based on the vehicle driving trajectory data; identifying the functional area locations of the expressway according to the road service facilities; and identifying the key hubs of the expressway in combination with the busy sections, the vehicle flow conditions and the functional area locations.

[0087] Among them, the traffic flow data refers to the information on the number of vehicles passing through a certain section of the expressway within a certain period of time, the vehicle driving trajectory data refers to the path information of the vehicle on the expressway, including the starting point, end point, driving route, etc. of the vehicle, the road service facilities refer to the facilities that provide services to expressway users, such as gas stations, service areas, toll stations, rest areas, etc., the busy section refers to the section on the expressway with large traffic flow, slow vehicle speed and serious traffic congestion, the vehicle flow situation refers to the operating status and distribution of vehicles on the expressway, such as vehicle speed, the functional area is located in the area with specific functions on the expressway, such as the location of the service area, the traffic flow refers to the number of vehicles passing through a certain section of the road per unit time, and the congestion index refers to the obstruction coefficient of traffic operation on the road.

[0088] Optionally, based on the vehicle driving trajectory data, the vehicle flow analysis of the highway can be obtained through vehicle positioning technology, such as GPS technology; based on the road service facilities, the functional area location identification of the highway can be achieved using a geographic information system; based on the traffic flow data, the traffic flow data of the highway can be determined by the number of vehicles passing through per unit time.

[0089] In an optional embodiment of the present invention, based on the traffic flow, the congestion index of the expressway is calculated using the following formula:

[0090] ;

[0091] Among them, I represents the congestion index of the highway, Q represents the traffic flow of the highway, A represents the maximum number of vehicles that can pass through per unit time, and L represents the length of the vehicle queue on the highway in the absence of congestion. It represents the maximum queue length of vehicles on the highway under the most congested conditions. It indicates the speed of vehicles on the highway without congestion. Indicates the speed of vehicles on a highway in congested conditions.

[0092] Furthermore, the embodiment of the present invention configures edge computing nodes of the highway at the key hub and obtains lightweight data packets of the highway based on the edge computing nodes, so that data processing tasks can be performed close to the data source, thereby reducing the delay in data transmission to the central server, reducing bandwidth consumption, and improving data processing efficiency. The edge computing node refers to a data computing device set up at the key hub of the highway, and the lightweight data packet refers to a compressed data packet that can reflect the current status of the highway in real time, such as a data packet consisting of traffic flow data and road condition data from 1:00 to 5:00 in the morning on the highway.

[0093] Optionally, according to the edge computing node, the lightweight data packet collection of the highway can be obtained by compressing and encapsulating the real-time feedback data of the highway.

[0094] S3. Generate cloud storage data of the highway according to the lightweight data packet, and analyze the traffic pattern of the highway based on the cloud storage data.

[0095] The embodiment of the present invention generates cloud storage data of the highway based on the lightweight data packet, which can help identify traffic patterns, predict traffic flow and optimize traffic management. The cloud storage data refers to key data such as traffic flow data, license plate recognition data, etc. that are uploaded to the cloud for storage after pre-processing, screening and analyzing the data generated by a large number of front-end devices on the highway through edge computing devices.

[0096] As an embodiment of the present invention, generating cloud storage data of the highway based on the lightweight data packet includes: extracting key real-time data of the highway based on the lightweight data packet; identifying the data value and data type of the key real-time data; setting the storage method of the key real-time data based on the data type; setting the data protection mechanism of the key real-time data based on the data value, and configuring the storage medium of the key real-time data; constructing a cloud storage path for the key real-time data based on the storage medium, the storage method and the data protection mechanism; and performing data storage processing of the key real-time data according to the cloud storage path to generate cloud storage data of the highway.

[0097] Among them, the key real-time data refers to data that needs to be collected, transmitted and processed in real time during the operation and management of highways, such as accident information data. The data value refers to the importance of data in a specific application scenario, such as the value of real-time traffic flow data for traffic congestion management. The data type refers to the format and content type of data, such as video, picture, table data, etc. The storage method refers to the organization and management method of data, such as incremental backup. The storage medium refers to the physical device or service used to store data, such as a solid-state drive. The data protection mechanism refers to the security measures and strategies used to protect sensitive data, such as data encryption. The cloud storage path refers to the storage location and path of data in the cloud storage system. The data storage processing refers to the process of transferring data from the edge device to the cloud for storage.

[0098] Optionally, the data value identification of the critical real-time data can be obtained by evaluating the importance of data through the hierarchical analysis method, and based on the data value, the data protection mechanism setting of the critical real-time data can be implemented using identity authentication, and based on the data value, the storage medium configuration of the critical real-time data can be implemented using a solid-state drive.

[0099] Furthermore, the embodiments of the present invention can predict the traffic flow change trend of the highway and identify abnormal situations on the highway by analyzing the traffic pattern of the highway based on the cloud storage data. The traffic pattern refers to the regular characteristics and behavior patterns of vehicle flow on the highway.

[0100] As an embodiment of the present invention, the analysis of the traffic pattern of the highway based on the cloud storage data includes: based on the cloud storage data, collecting historical traffic flow data of the highway, and extracting traffic flow characteristics of the historical traffic flow data; analyzing the current traffic conditions of the highway according to the traffic flow characteristics; identifying influencing factors and key mode turning points of the current traffic conditions; based on the key mode turning points, extracting the classification mode representative threshold of the highway; and analyzing the traffic pattern of the highway in combination with the influencing factors, the key mode turning points and the classification mode representative threshold.

[0101] Among them, the historical traffic flow data refers to the traffic flow data collected over a period of time in the past, such as data on historical congestion periods and areas; the traffic flow characteristics refer to parameters or indicators that describe traffic flow characteristics, such as changes in vehicle density; the current traffic conditions refer to the actual traffic conditions of the expressway at a specific time point or time period, including traffic volume, congestion, accidents and incidents, etc.; the influencing factors refer to various factors that can affect traffic conditions, such as weather conditions, driving behavior, road conditions, etc.; the key mode turning point refers to the critical point where the traffic condition changes from one stable state to another, such as the congestion relief point; the classification mode representative threshold refers to the critical value used to distinguish different traffic modes or states, such as flow threshold, speed threshold, etc.

[0102] Optionally, based on the traffic flow characteristics, the current traffic condition analysis of the highway can be obtained through an ARIMA model, the identification of influencing factors of the current traffic condition can be achieved using data analysis software, such as SPSS software, the identification of key mode turning points of the current traffic condition can be obtained through VISSIM traffic simulation software, and the classification mode representative threshold extraction of the highway based on the key mode turning points can be achieved using a vehicle positioning system.

[0103] S4. According to the traffic pattern, identify the traffic flow change trend of the expressway, analyze the abnormal traffic conditions of the expressway based on the traffic flow change trend, and set up an abnormal self-service processing mechanism and intelligent assistance system for the expressway according to the abnormal traffic conditions.

[0104] The embodiment of the present invention can predict future traffic flow and congestion by identifying the traffic flow change trend of the highway according to the traffic pattern, and prepare countermeasures in advance. It also helps the cloud control system to respond to traffic events in real time and reduce the accident rate. The traffic flow change trend refers to the increase or decrease and development direction of traffic flow over time within a certain time and space range.

[0105] As an embodiment of the present invention, identifying the traffic flow change trend of the highway according to the traffic pattern includes: collecting traffic data of the highway according to the traffic pattern; creating a time series graph of the highway based on the traffic data; identifying the vehicle type distribution and peak and valley time periods of the highway according to the time series graph; analyzing the hot spot congestion areas of the highway based on the vehicle type distribution and the peak and valley time periods; collecting historical traffic data of the hot spot congestion areas, and identifying the traffic flow change trend of the highway according to the historical traffic data.

[0106] Among them, the traffic data refers to various data generated during the operation of the highway, such as vehicle driving trajectory data; the time series graph refers to a graph that arranges data in chronological order and draws the data change trend on the coordinate axis; the vehicle type distribution refers to the proportion of different types of vehicles (such as cars, trucks, buses, etc.) in a certain period of time on the highway; the peak and valley time periods refer to the peak and valley periods of highway traffic flow in a day; the hot spot congestion area refers to the area where traffic congestion often occurs on the highway; the historical traffic data refers to the highway traffic data collected in the past period of time, including vehicle flow, speed, event records, etc.

[0107] Optionally, based on the traffic data, the creation of the time series graph of the highway can be implemented by using a drawing library, such as the Matplotlib drawing library; based on the time series graph, the peak and valley time periods of the highway can be identified by an average value calculation formula; based on the vehicle type distribution and the peak and valley time periods, the hot spot congestion area of ​​the highway can be extracted using a spatiotemporal data field method; based on the historical traffic data, the identification of traffic flow change trends on the highway can be obtained through a neural network model.

[0108] Furthermore, the embodiments of the present invention analyze the abnormal traffic conditions on the highway based on the traffic flow change trend, which helps to investigate accidents, determine the causes of accidents, improve road safety, and provide drivers with real-time abnormal traffic condition information to help them make better route choices to achieve efficient resource scheduling. The abnormal traffic conditions refer to non-normal or unexpected events occurring on the highway, such as traffic accidents, vehicle failures, traffic signal failures, etc.

[0109] Optionally, the abnormal traffic situation analysis of the expressway based on the traffic flow change trend can be obtained through surveillance cameras on the expressway.

[0110] The embodiment of the present invention can reduce the time of manual intervention and improve the processing speed by setting up the abnormal self-service processing mechanism of the highway according to the abnormal traffic conditions, so as to reduce the risk of secondary accidents and improve road safety. At the same time, it can also reduce the workload of the traffic management center and reduce operating costs. The abnormal self-service processing mechanism refers to an automated processing system that can automatically detect, evaluate and respond to abnormal traffic conditions.

[0111] As an embodiment of the present invention, the abnormal self-service processing mechanism for the highway is set according to the abnormal traffic situation, including: identifying the event type of the abnormal traffic situation; setting the alarm trigger mode of the abnormal traffic situation according to the event type; identifying the development trend of the abnormal traffic situation based on the alarm trigger mode; constructing a linkage control network for the abnormal traffic situation according to the development trend; analyzing the urgency of the event type, and defining the remote response method and automatic adjustment mechanism of the linkage control network according to the urgency and the event type; and setting the abnormal self-service processing mechanism for the highway in combination with the alarm trigger mode, the remote response method and the automatic adjustment mechanism.

[0112] Among them, the event type refers to the specific classification of abnormal traffic conditions, such as vehicle failure; the alarm trigger mode refers to the rules or conditions used to identify when the alarm should be triggered, such as detecting a vehicle collision; the development trend refers to the possible changing trend of abnormal traffic conditions over time, such as the spread of congestion; the linkage control network refers to a network that integrates multiple traffic management systems and equipment to collaboratively respond to abnormal traffic conditions; the urgency refers to the degree of impact that the event may cause; the remote response method refers to the response measures taken by the traffic management center through remote operation, such as remote release of traffic information and warnings; the automatic adjustment mechanism refers to a mechanism for real-time adjustment based on the lightweight data packets provided by the alarm system, such as automatically adjusting road operation strategies according to weather conditions.

[0113] Optionally, according to the event type, the alarm trigger mode setting for the abnormal traffic situation can be obtained through an event detection algorithm, according to the urgency and the event type, the remote response method definition of the linkage control network can be implemented by using a traffic management center, according to the development trend, the linkage control network construction for the abnormal traffic situation can be obtained by integrating multiple traffic systems, such as vehicle communication systems, emergency service communication systems, variable information sign systems, etc., and according to the urgency and the event type, the automatic adjustment mechanism definition of the linkage control network can be implemented by using the system's adaptive algorithm.

[0114] Furthermore, the embodiments of the present invention can reduce manual intervention, improve travel efficiency, and improve the driver's travel experience by setting up an intelligent assistance system for the highway according to the abnormal traffic conditions. The intelligent assistance system refers to an auxiliary system that integrates multiple intelligent technologies and automated equipment to improve the operational management efficiency and road safety of highways, such as intelligent road gates.

[0115] As an embodiment of the present invention, the intelligent assistance system of the expressway is set up according to the abnormal traffic situation, including: identifying the abnormal response capability of the expressway according to the abnormal traffic situation; setting up an intelligent lane guard booth of the expressway based on the abnormal response capability; locating the vehicle entry and exit points of the expressway, and setting the vehicle travel interference reminder of the expressway according to the vehicle entry and exit points; identifying the abnormal lane of the expressway based on the intelligent lane guard booth and the vehicle travel interference reminder, and setting a backup lane for the abnormal lane; identifying the lane interception device of the expressway, and defining a release and payment method for the lane interception device; and setting up the intelligent assistance system of the expressway in combination with the intelligent lane guard booth, the backup lane and the release and payment method.

[0116] Among them, the abnormal response capability refers to the ability of the highway management system to quickly respond to and handle abnormal traffic conditions, the smart lane guard booth refers to a highway booth equipped with advanced information technology and communication equipment, the vehicle entry and exit point refers to the vehicle entering or leaving the entrance ramp and exit ramp of the highway, the vehicle travel interference reminder refers to a notification device that provides drivers with information that may affect the travel through various communication channels, the abnormal lane refers to a lane that cannot be used normally due to an abnormal event, the spare lane refers to a lane that provides an alternative route for vehicles when the abnormal lane cannot be used, the lane interception device refers to a physical barrier or mechanical device used to control the passage of vehicles, such as a lane gate, and the release and payment method refers to the method of paying tolls when a vehicle passes through a toll station.

[0117] Optionally, based on the abnormal response capability, the intelligent lane guard booth setting of the highway can be implemented using a smart booth, the release and payment method definition of the lane interception device can be obtained through a multi-functional intelligent payment terminal, such as UnionPay payment, and according to the vehicle entry and exit points, the vehicle travel interference reminder setting of the highway can be implemented using communication channels, such as broadcasting, mobile applications, etc.

[0118] S5. Based on the abnormal self-service processing mechanism and the intelligent assistance system, define the traffic management mode of the expressway, set the user interaction mechanism of the expressway according to the traffic management mode, and build a data sharing platform for the expressway based on the traffic management mode and the user interaction mechanism.

[0119] The embodiment of the present invention can achieve global optimization of the traffic management system and improve the traffic management capability of the highway by defining the traffic management mode of the highway based on the abnormal self-service processing mechanism and the intelligent assistance system. The traffic management mode refers to a method for managing and controlling traffic flow.

[0120] As an embodiment of the present invention, the traffic management mode of the expressway is defined based on the abnormal self-service processing mechanism and the intelligent assistance system, including: identifying abnormal traffic conditions on the expressway based on the abnormal self-service processing mechanism; analyzing the causes of the abnormal traffic conditions, and setting the adaptive control method of the expressway according to the causes; setting the information interaction mechanism of the expressway based on the abnormal traffic conditions and the adaptive control method; identifying the emergency response rules of the expressway according to the intelligent assistance system; and defining the traffic management mode of the expressway in combination with the emergency response rules, the adaptive control method and the information interaction mechanism.

[0121] Among them, the abnormal traffic conditions refer to situations that deviate from the normal traffic flow state during the operation of the highway, such as traffic accidents, vehicle breakdowns, etc. The causes of formation refer to various factors that lead to abnormal traffic conditions on highways, such as holiday travel peaks. The adaptive control method refers to a method of automatically adjusting traffic control strategies according to dynamic changes in traffic flow. The information interaction mechanism refers to the rules and methods for information transmission and sharing between subsystems, equipment and participants in the highway traffic management system. The emergency response rules refer to the emergency handling rules that the traffic management department and relevant units should follow when emergencies or abnormal traffic conditions occur on highways.

[0122] Optionally, according to the formation cause, the adaptive control mode setting of the highway can be implemented by using an adaptive control algorithm, and based on the abnormal traffic condition and the adaptive control mode, the information interaction mechanism setting of the highway can be determined by an information interaction platform, such as an Open Traffic Collection platform.

[0123] Furthermore, the embodiments of the present invention can provide real-time traffic information and guidance, increase the driver's understanding of current traffic conditions, improve information transparency and driver response speed, and optimize traffic flow management by setting the user interaction mechanism of the highway according to the traffic management mode. The user interaction mechanism refers to the method used in the highway cloud control system to exchange information and interact with drivers and passengers.

[0124] Optionally, according to the traffic management mode, the user interaction mechanism setting of the highway can be implemented by using variable information signs, such as electronic signs beside the highway.

[0125] The embodiment of the present invention constructs a data sharing platform for the highway based on the traffic management model and the user interaction mechanism. Information can be exchanged through the data sharing platform to achieve cross-departmental collaborative work, improve the overall efficiency of traffic management, and monitor road safety conditions in real time through integrated monitoring data, quickly respond to traffic accidents and emergencies, and help decision makers make faster and more accurate decisions. The data sharing platform refers to a system that integrates highway-related data and realizes safe and efficient sharing of data among different departments, institutions and users through specific user interaction methods.

[0126] As an embodiment of the present invention, the data sharing platform of the expressway is constructed based on the traffic management model and the user interaction mechanism, including: based on the traffic management model, collecting the integrated traffic data of the expressway, and constructing a data collection library of the integrated traffic data; based on the user interaction mechanism, identifying the demand users of the data collection library; analyzing the data sensitivity of the data collection library, and setting the data access scope and identity recognition password of the demand users according to the data sensitivity; based on the data access scope and identity recognition password, setting the data query entry and data display interface of the data collection library; combining the data query entry and the data display interface to generate the data sharing platform of the expressway.

[0127] Among them, the integrated traffic data refers to traffic flow data collected from different sources and systems of highways, such as surveillance videos and images; the data collection library refers to a centralized data storage system; the data sensitivity refers to the confidentiality level and importance of the data; the required user refers to the user or system that needs to access the data in the data collection library; the data access scope refers to the boundary of the data set in the data collection library that the user is authorized to access; the identity identification password refers to information used to verify the identity of the user, such as a user name and password; the data query portal refers to the interface for users to interact with the data collection library; and the data display interface refers to an interface that displays the data in the data collection library to the user in the form of graphics, charts and reports.

[0128] Optionally, the construction of the data collection library integrating traffic data can be implemented by using a data warehouse, and the data query entry setting of the data collection library based on the data access scope and identity recognition password can be obtained through a self-service query terminal device, such as a multimedia query terminal, and the data access scope setting of the required user according to the data sensitivity can be implemented by using an access control list.

[0129] S6. In combination with the full-area road perception network, the traffic flow change trend and the data sharing platform, traffic prediction processing of the expressway is performed to obtain a prediction processing result.

[0130] The embodiment of the present invention combines the full-area road sensing network, the traffic flow change trend and the data sharing platform to perform traffic forecasting processing on the highway to obtain forecasting processing results, which can improve the input data quality of the traffic forecasting model, thereby improving the accuracy of traffic forecasting. At the same time, it can also help traffic management departments to reasonably allocate traffic resources, realize intelligent management of highways, and improve the management efficiency and accuracy of highways, so as to enhance the country's governance capabilities in the field of transportation and promote economic growth. The traffic forecasting processing refers to the process of predicting the future traffic conditions of the highway and performing corresponding processing and decision-making based on the forecast results. The forecasting processing results refer to the output information obtained after analyzing and predicting the future traffic conditions of the highway, such as the section and duration of congestion.

[0131] Compared with the problems described in the background technology, the embodiment of the present invention can collect traffic data on the road in real time and improve the intelligence level of road traffic by constructing a full-area road perception network of the highway based on the data acquisition equipment; further, the embodiment of the present invention can configure the edge computing node of the highway at the key hub, and obtain the lightweight data packet of the highway according to the edge computing node, so that the data processing task can be carried out close to the data source, thereby reducing the delay in data transmission to the central server, reducing bandwidth consumption, and improving data processing efficiency; secondly, the embodiment of the present invention can help identify traffic patterns, predict traffic flow and optimize traffic management by generating cloud storage data of the highway according to the lightweight data packet; thirdly, the embodiment of the present invention can reduce the time of manual intervention and improve the processing speed by setting up the abnormal self-service processing mechanism of the highway according to the abnormal traffic conditions, so as to reduce the risk of secondary accidents and improve road safety, and at the same time, it can also reduce the workload of the traffic management center and reduce operating costs, and by setting up the intelligent auxiliary system of the highway, it can reduce manual intervention, improve travel efficiency, and improve the travel experience of drivers; in addition, the embodiment of the present invention can reduce the time of manual intervention and improve the travel efficiency by setting up the intelligent auxiliary system of the highway according to the abnormal self-service processing mechanism. The auxiliary processing mechanism and the intelligent auxiliary system define the traffic management mode of the expressway, set the user interaction mechanism of the expressway, can provide real-time traffic information and guidance, increase the driver's understanding of the current traffic conditions, improve information transparency and driver response speed, and achieve global optimization of the traffic management system; further, the embodiment of the present invention builds a data sharing platform for the expressway based on the traffic management mode and the user interaction mechanism, can exchange information through the data sharing platform, realize cross-departmental collaborative work, improve the overall efficiency of traffic management, and through integrated monitoring data, monitor road safety conditions in real time, quickly respond to traffic accidents and emergencies, and help decision makers make faster and more accurate decisions; finally, the embodiment of the present invention performs traffic prediction processing on the expressway by combining the full-area road perception network, the traffic flow change trend and the data sharing platform to obtain the prediction processing result, which can improve the input data quality of the traffic prediction model, thereby improving the accuracy of traffic prediction, and can also help the traffic management department to reasonably allocate traffic resources, realize intelligent management of expressways, and improve the management efficiency and accuracy of expressways, so as to enhance the country's governance capabilities in the field of transportation and promote economic growth. Therefore, the cloud-edge-based highway traffic prediction method and system provided by the embodiments of the present invention can improve the prediction efficiency and accuracy of the highway traffic prediction system.

[0132] Example 2

[0133] like Figure 2Shown is a functional module diagram of a cloud-edge-based highway traffic prediction system of the present invention.

[0134] The cloud-edge-based highway traffic prediction system 200 of the present invention can be installed in an electronic device. According to the functions implemented, the cloud-edge-based highway traffic prediction system can include a road perception module 201, an edge computing module 202, a cloud data analysis module 203, a traffic trend analysis module 204, a data sharing module 205 and a traffic prediction module 206. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0135] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0136] The road sensing module 201 is used to obtain the highway to be managed, configure the data acquisition equipment of the highway, and build a full-area road sensing network of the highway based on the data acquisition equipment;

[0137] The edge computing module 202 is used to identify the key hubs of the expressway based on the full-area road perception network, configure the edge computing nodes of the expressway at the key hubs, and obtain the lightweight data packets of the expressway according to the edge computing nodes;

[0138] A cloud data analysis module 203, configured to generate cloud storage data of the highway according to the lightweight data packet, and analyze the traffic pattern of the highway based on the cloud storage data;

[0139] The traffic trend analysis module 204 is used to identify the traffic flow change trend of the expressway according to the traffic mode, analyze the abnormal traffic situation of the expressway based on the traffic flow change trend, and set the abnormal self-service processing mechanism and intelligent assistance system of the expressway according to the abnormal traffic situation;

[0140] The data sharing module 205 is used to define the traffic management mode of the expressway based on the abnormal self-service processing mechanism and the intelligent assistance system, set the user interaction mechanism of the expressway according to the traffic management mode, and build a data sharing platform for the expressway based on the traffic management mode and the user interaction mechanism;

[0141] The traffic prediction module 206 is used to perform traffic prediction processing on the expressway in combination with the whole-area road perception network, the traffic flow change trend and the data sharing platform to obtain a prediction processing result.

[0142] In detail, each module in the cloud-edge-based highway traffic prediction system 200 in the embodiment of the present invention is used in the same manner as described above. Figure 1 The cloud-edge-based highway traffic prediction method described in the text is the same technical means and can produce the same technical effects, so I will not go into details here.

[0143] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A highway traffic prediction method based on cloud-edge, characterized in that: Methods include: S1. Obtain the highway to be managed, configure the data collection equipment of the highway, and build a full-area road perception network of the highway based on the data collection equipment; S2. Based on the full-area road perception network, identify the key hubs of the expressway, configure the edge computing nodes of the expressway at the key hubs, and obtain the lightweight data packets of the expressway based on the edge computing nodes, where the lightweight data packets refer to compressed data packets that can reflect the current status of the expressway in real time; based on the full-area road perception network, collect the traffic flow data, vehicle driving trajectory data and road service facilities of the expressway; based on the traffic flow data, collect the traffic flow of the expressway, and based on the traffic flow, calculate the congestion index of the expressway using the following formula: ； Among them, I represents the congestion index of the highway, Q represents the traffic flow of the highway, A represents the maximum number of vehicles that can pass through per unit time, and L represents the length of the vehicle queue on the highway in the absence of congestion. represents the maximum queue length of vehicles on the highway under the most congested conditions, V represents the speed of vehicles on the highway under non-congested conditions, It indicates the speed of vehicles on the highway in congestion; Identify busy sections of highways based on traffic flow and congestion index; analyze vehicle flow on highways based on vehicle trajectory data; identify functional area locations on highways based on road service facilities; functional area locations are locations of areas with specific functions on highways; identify key hubs on highways based on busy sections, vehicle flow, and functional area locations; S3. Generate cloud storage data of the highway based on the lightweight data packet, where the cloud storage data refers to the key data generated by a large number of front-end devices on the highway, which is pre-processed, screened and analyzed by edge computing devices and then uploaded to the cloud for storage. Based on the cloud storage data, analyze the traffic pattern of the highway; S4. Identify the traffic flow change trend of the expressway according to the traffic mode, analyze the abnormal traffic conditions of the expressway based on the traffic flow change trend, and set up the abnormal self-service processing mechanism and intelligent assistance system of the expressway according to the abnormal traffic conditions; S5. Based on the abnormal self-service processing mechanism and intelligent assistance system, define the traffic management mode of the expressway, set the user interaction mechanism of the expressway according to the traffic management mode, and build the data sharing platform of the expressway based on the traffic management mode and the user interaction mechanism; S6. Combine the regional road perception network, traffic flow change trend and data sharing platform to perform traffic prediction processing on the expressway and obtain the prediction processing results.

2. The cloud-edge-based highway traffic prediction method according to claim 1, characterized in that: Based on data collection equipment, a full-area road perception network for expressways is constructed, including: Identify traffic participants on highways based on data collection equipment; Collecting the perception data of traffic participants and setting up a data processing unit for the perception data; According to the data collection equipment and data processing unit, the information transmission layer of the expressway is set up; Based on the information transmission layer, identify the transmission data value of the highway; According to the value of the transmitted data, set the application terminal of the highway; Combining data collection equipment, data processing units, information transmission layers and application terminals, a full-area road perception network for expressways is constructed.

3. The cloud-edge-based highway traffic prediction method according to claim 1, characterized in that: Analyze highway traffic patterns based on cloud storage data, including: Based on cloud storage data, collect historical traffic flow data of highways and extract traffic flow characteristics of historical traffic flow data; Analyze the current traffic conditions of the highway based on traffic flow characteristics; Identify the influencing factors and key mode turning points of current traffic conditions; Based on the key mode turning points, the representative threshold of the classification mode of the highway is extracted; The traffic patterns of highways are analyzed by combining influencing factors, key mode turning points and representative thresholds of classification modes.

4. The cloud-edge-based highway traffic prediction method according to claim 1, characterized in that: Identify traffic flow trends on highways based on traffic patterns, including: Collecting highway traffic data based on traffic patterns; Create a time series graph of highways based on traffic data; Based on the time series graph, identify the vehicle type distribution and peak and valley periods on the highway; Analyze the hotspot congestion areas on highways based on the distribution of vehicle types and peak and valley time periods; Collect historical traffic data from hotspot congestion areas and identify traffic flow change trends on highways based on historical traffic data.

5. The cloud-edge-based highway traffic prediction method according to claim 1, characterized in that: According to abnormal traffic conditions, set up an abnormal self-service handling mechanism for expressways, including: Identify event types of abnormal traffic situations; Set alarm triggering mode for abnormal traffic conditions according to event type; Identify the development trend of abnormal traffic conditions based on alarm triggering patterns; According to the development trend, a linkage control network for abnormal traffic conditions is constructed; Analyze the urgency of the event type, and define the remote response method and automatic adjustment mechanism of the linkage control network based on the urgency and event type; Combining the alarm trigger mode, remote response method and automatic adjustment mechanism, a self-service abnormality handling mechanism for highways is set up.

6. The cloud-edge-based highway traffic prediction method according to claim 1, characterized in that: According to abnormal traffic conditions, an intelligent assistance system for highways is set up, including: Identify abnormal response capabilities on highways based on abnormal traffic conditions; Based on the abnormal response capability, set up intelligent lane guard booths on highways; Locate the vehicle entry and exit points of the highway, and set the vehicle travel interference reminder of the highway according to the vehicle entry and exit points; Based on the intelligent lane guard booth and vehicle travel interference reminder, abnormal lanes on the highway are identified and backup lanes are set for abnormal lanes; Identify lane blocking devices on highways and define the release and payment methods for lane blocking devices; An intelligent assistance system for highways is set up in combination with smart lane guard booths, spare lanes and release and payment methods.

7. The cloud-edge-based highway traffic prediction method according to claim 1, characterized in that: Based on the abnormal self-service processing mechanism and intelligent assistance system, the traffic management mode of the highway is defined, including: Identify abnormal traffic conditions on highways based on an abnormal self-service processing mechanism; Analyze the causes of abnormal traffic conditions and set the adaptive control mode of the highway according to the causes; Set up an information interaction mechanism for expressways based on abnormal traffic conditions and adaptive control methods; Identify emergency response rules for highways based on the intelligent assistance system; The traffic management mode of the highway is defined by combining emergency response rules, adaptive control methods and information interaction mechanisms.

8. The cloud-edge-based highway traffic prediction method according to claim 1, characterized in that: Based on the traffic management model and user interaction mechanism, a data sharing platform for highways is constructed, including: Based on the traffic management model, collect integrated traffic data of highways and build a data collection database of integrated traffic data; Based on the user interaction mechanism, identify the users who need the data collection library; Analyze the data sensitivity of the data collection library, and set the data access scope and identity identification password of the required users according to the data sensitivity; Based on the data access scope and identity identification password, set up the data query entry and data display interface of the data collection library; Combine the data query portal and data display interface to generate a highway data sharing platform.

9. A highway traffic prediction system based on cloud-edge terminal, the system implements the method as claimed in claim 1, characterized in that the system include: The road perception module is used to obtain the highways to be managed, configure the data collection equipment of the highways, and build a road perception network for the entire area of ​​the highways based on the data collection equipment; The edge computing module is used to identify the key hubs of the expressway based on the full-area road perception network, configure the edge computing nodes of the expressway at the key hubs, and obtain the lightweight data packets of the expressway based on the edge computing nodes; A cloud data analysis module is used to generate cloud storage data of the highway according to the lightweight data packet, and analyze the traffic pattern of the highway based on the cloud storage data; Traffic trend analysis module, used to identify the traffic flow change trend of the expressway according to the traffic mode, analyze the abnormal traffic situation of the expressway based on the traffic flow change trend, and set up the abnormal self-service processing mechanism and intelligent assistance system of the expressway according to the abnormal traffic situation; The data sharing module is used to define the traffic management mode of the expressway based on the abnormal self-service processing mechanism and the intelligent assistance system, set the user interaction mechanism of the expressway according to the traffic management mode, and build a data sharing platform for the expressway based on the traffic management mode and the user interaction mechanism; The traffic prediction module is used to combine the regional road perception network, traffic flow change trends and data sharing platform to perform traffic prediction processing on the highway and obtain the prediction processing results.

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