2052 results about "Traffic management" patented technology

The invention discloses a smart road driving time prediction method based on multi-source data fusion and deep learning, and the method comprises the steps: constructing a multi-source data collection and fusion platform, developing a spatial-temporal feature deep learning model, deploying a real-time data processing and anomaly detection system, building a self-adaptive prediction optimization mechanism, and constructing a smart traffic application service platform. Comprehensive acquisition, deep analysis, real-time processing, accurate prediction and intelligent service of traffic data are realized. The method specifically comprises the following steps: deploying an Internet of Things sensor network to collect multi-source data, and designing an adaptive weighted fusion algorithm to construct a multi-dimensional dynamic traffic database; constructing an LSTM-GNN hybrid neural network architecture to extract spatio-temporal features; real-time data processing and anomaly detection are carried out by adopting a streaming computing framework and various technologies; designing a multi-objective optimization algorithm and a reinforcement learning model to realize adaptive prediction optimization; and developing a visual decision support system and a personalized navigation service. The system effectively improves the traffic management efficiency and the public travel experience.

An intelligent traffic management device for optimizing traffic flow at one or more intersections using reinforcement learning, comprising: a central control unit housed in a weatherproof enclosure and mounted on a structural support near an intersection; a sensor interface operatively connected to a variety of multimodal sensors, including at least radar-based vehicle detectors, inductive loop sensors embedded in the road surface, infrared pedestrian detection detectors, and high-resolution optical imaging modules; a powerful embedded computing unit consisting of a multi-core processor and a GPU configured for on-device inference; a reinforcement learning engine embedded in the computing unit, comprising a deep reinforcement learning model trained to map environmental states defined by traffic-related sensor inputs to optimized action policies for traffic light control; a signal actuation module electrically connected to traffic signal controllers, with action policies outputting signal phase durations and transitions in real time; a communications module supporting wireless connectivity and configured for device-to-device communication and cloud synchronization; a policy synchronization module configured to perform federated learning-based updates of model parameters by exchanging gradient weights or distilled traffic state vectors with neighboring intelligent traffic management devices deployed in a traffic network; where the reinforcement learning engine continuously observes traffic state vectors consisting of vehicle counts, average speeds, queue lengths, and pedestrian activity, and dynamically updates the policy to maximize cumulative traffic throughput and minimize average delay.

The invention discloses a guardrail collision warning method and system, and the method comprises the steps: carrying out the nonlinear phase alignment of a multi-source heterogeneous signal through an adaptive variational mode decomposition algorithm according to the vibration acceleration, strain tensor and acoustic emission signals collected in real time through a multi-mode sensor array disposed at a guardrail key node, generating a three-dimensional dynamic strain field distribution vector; inputting the three-dimensional dynamic strain field distribution vector into a nonlinear dynamics reconstruction module, and extracting a chaotic feature fingerprint spectrum of the collision event; performing collision intensity grading processing on the chaos feature fingerprint spectrum, and outputting a quantitative evaluation matrix including a collision grade, a damage radius and a residual intensity prediction value; and triggering a multi-mode alarm protocol according to the quantitative evaluation matrix, and synchronously transmitting the multi-mode alarm protocol to a traffic management center and an adjacent vehicle OBU terminal. According to the embodiment of the invention, the state of the guardrail can be monitored in real time, and a collision event can be accurately evaluated and warned.

The invention relates to the technical field of traffic management, in particular to a smart city intelligent traffic management system based on big data, which comprises a multi-source data acquisition module, a data fusion module, a dynamic feature generation module, a situation prediction module, a multi-target optimization module and an instruction execution module, the multi-source data acquisition module acquires multi-source data in real time; the data fusion module generates a standardized traffic data flow and outputs a three-dimensional data cube; the dynamic feature generation module outputs a traffic feature matrix; the situation prediction module generates a dynamic situation map; the multi-objective optimization module outputs an optimization instruction set; and the instruction execution module issues a control instruction to the traffic signal controller, the variable information board and the vehicle-mounted terminal. According to the invention, traffic jam can be relieved, road traffic efficiency can be improved, bus priority scheduling can be optimized, vehicle delay can be reduced, and emergency response speed can be improved.

The disclosure provides an intelligent horizontal transportation system for automatic loading or unloading at a container terminal. The system includes a plurality of autonomous transport robots (ATRs) and an ATR control system. The plurality of ATRs are configured to perform horizontal transportation tasks. The ATR control system is in real-time communication with the plurality of ATRs to manage and control the plurality of ATRs; the ATR control system is in real-time communication with a terminal management system, an automated yard crane, and an automated quay crane, so as to coordinate task scheduling between the automated yard cranes, automated quay cranes, and the plurality of ATRs. The ATR control system includes a task scheduling module, a dynamic path planning module, a standardized control interface module, a traffic management module, a lock station management module, a vehicle sequencing module, a charging scheduling module, a parking management module, and a remote driving module.

The invention provides an edge computing traffic light intelligent decision-making method integrated with AI video analysis, and the method comprises the steps: collecting a real-time traffic video stream of a target intersection through an edge computing device, and extracting a dynamic traffic feature set in the real-time traffic video stream, calling a pre-trained space-time analysis model to carry out multi-modal fusion processing on the dynamic traffic characteristic set, generating a traffic state vector of the target intersection, matching a candidate control strategy in a preset decision rule base based on the traffic state vector, and carrying out parameter adjustment on the candidate control strategy through a strategy optimization model to obtain a target traffic state vector; generating a target signal lamp control parameter; and issuing the target signal lamp control parameters to a traffic signal control terminal of the target intersection, and monitoring traffic state change data of the target intersection in real time to update weight parameters of the space-time analysis model. According to the invention, the traffic efficiency, pedestrian safety and traffic management intelligence degree of urban intersections can be improved.

The invention relates to the technical field of traffic management, in particular to a video monitoring traffic intelligent optimization system which comprises a time sequence fragment evaluation module, a path priority adjustment module, a traffic scheduling optimization module and a buffer strategy adaptation module. According to the invention, the transmission efficiency and quality of the video monitoring data are improved by analyzing the video stream in real time and adjusting the frame rate and the compression rate, the importance of each time sequence segment in the video stream is evaluated, the frame rate is adjusted according to the importance, more resources are ensured to be obtained by important events, unnecessary data transmission is reduced, network congestion is effectively reduced, and the transmission efficiency of the video monitoring data is improved. The network transmission path is monitored in real time, and the data transmission proportion is adjusted, so that the video stream passes through the network path with higher quality more preferentially, the stability and speed of video transmission are enhanced, the priority and bandwidth allocation are dynamically adjusted, and the video stream with high priority is protected to ensure that the video quality of a key monitoring area is not influenced by network fluctuation.

The present invention relates to the technical field of highway maintenance management, and in particular to a highway maintenance safety management system. The system includes a road condition monitoring module, a traffic load assessment module, a traffic flow redistribution module, a maintenance timing determination module, and a maintenance effect verification module, which are used to monitor multi-layer road condition data of the highway, and perform table-deep correlation detection to obtain road surface abnormal point data; quantify the road surface loss risk of the road surface abnormal point data to obtain road surface loss early warning indicators; collect vehicle images and meteorological data; analyze vehicle model loss contribution based on vehicle images; construct an environmental impact coefficient matrix based on meteorological data; construct a dynamic weight model based on vehicle model loss contribution and environmental impact coefficient matrix to obtain a road surface loss dynamic weight model. The present invention improves the efficiency, benefit and intelligence level of highway maintenance management by organically integrating highway maintenance with traffic management.

The invention discloses a control strategy model construction method based on big data, and relates to the technical field of intelligent control, a traffic situation deduction and emergency strategy generation module uses a multi-agent traffic simulation technology, simulates a traffic situation in combination with real-time traffic data, evaluates risks and early warns potential crisis by integrating multiple factors, and provides a control strategy model for the traffic situation deduction and emergency strategy generation module. And after an early warning is received, an emergency strategy is generated by using an intelligent algorithm, the strategy is evaluated and optimized by using virtual rehearsal, a result is fed back to a management department, and a synergistic effect with other modules is achieved. Traditional traffic detection equipment data is integrated with multi-source heterogeneous data such as mobile phone signaling, shared bicycle and online hailed bicycle GPS, intelligent vehicle-mounted equipment and public traffic operation, more comprehensive and accurate information such as traffic flow, flow direction, travel behavior track and the like is provided, and rich and accurate basis is provided for traffic management decisions.

The invention discloses a highway data fusion management method, and relates to the technical field of intelligent traffic management, and the method comprises the steps: collecting the global operation data of a highway in real time through a multi-source heterogeneous sensing network; performing space-time alignment and semantic annotation processing on the operation data to construct a fusion data set; inputting the feature subset into a lightweight space-time fusion model to form a multi-target decision set; executing the traffic control instruction sequence in a preset space-time window according to the multi-target decision set; and generating a self-optimization report including a model iteration path and road network health assessment based on the deviation degree of the execution feedback data and the expected optimization target. According to the expressway data fusion management method provided by the invention, collaborative optimization of passing efficiency and safety of the expressway is guaranteed.

The invention provides an intelligent traffic dynamic green wave control method and system, a storage medium and a program product, and relates to the field of traffic control systems.The method comprises the steps that real-time traffic data of all lanes and emergency data published by a traffic management department are obtained; obtaining traffic flow prediction results of different directions of each road section in a future preset time based on a neural network model; outputting the congestion level and the congestion duration of each road section in combination with the emergency situation data and a traffic flow theoretical model, and further determining an adjustment sequence; and on the basis of the traffic flow prediction result, calculating the optimal green wave bandwidth of the signal lamp at each intersection in each target road section by using a particle swarm optimization algorithm to obtain an optimal timing combination scheme of the signal lamp in each target road section, and performing real-time timing control. By implementing the method, the traffic flow can be dredged more efficiently, the parking frequency and delay time of vehicles on the road are reduced, the overall traffic capacity of the urban road is improved, and energy consumption and environmental pollution are reduced.

The invention relates to the technical field of smart city traffic management, in particular to an urban traffic jam intelligent prediction and dispersion system and a method thereof, and the system integrates a data acquisition module, a traffic prediction module, an event influence evaluation module and an intelligent dispersion module. The data acquisition module updates multi-source traffic data in real time, and a solid foundation is provided for prediction; and the traffic prediction module accurately predicts the traffic flow by using a deep learning model combining a graph neural network and a space-time attention mechanism based on the received data. The event influence evaluation module analyzes the influence of emergencies on traffic and ensures the pertinence of a dispersion strategy; the intelligent dispersion module optimizes a traffic signal lamp control scheme in real time by using deep reinforcement learning in combination with the prediction result and the event influence; the system comprehensively senses the traffic condition through multi-source heterogeneous data fusion, the traffic flow prediction accuracy is remarkably improved, and intelligent and efficient traffic management is achieved.

The invention provides a high-precision perception-driven intelligent road network collaborative optimization method and system, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting road sensing data through multi-sensor fusion, and obtaining multi-source perception data; performing dynamic traffic flow modeling according to the multi-source sensing data to obtain a traffic state prediction result; performing optimization decision on the traffic state prediction result to obtain an intelligent optimization decision; performing road network level cooperative control on the intelligent optimization decision to obtain a road network level cooperative control result; and performing real-time dynamic adjustment by using the road network level cooperative control result to obtain a road network scheduling result. According to the method and the device, the technical targets of global collaborative scheduling, improvement of traffic flow prediction precision, optimization of a real-time dynamic scheduling scheme and improvement of the intelligent level of urban traffic management can be realized, and the technical effects of reducing traffic congestion, improving road traffic efficiency, optimizing traffic resource allocation and improving emergency response capability are achieved.

The invention belongs to the technical field of ship collision avoidance, discloses a ship collision avoidance early warning method and system based on vision and AIS data collaborative perception, and firstly provides an enhanced YOLO (E-YOLO) target detector which can detect a ship in a complex environment in a high-precision and real-time manner and perform ship tracking and visual trajectory extraction in combination with a DeepSORT algorithm. Secondly, a homography coordinate conversion method based on multiple frames of static images is provided, conversion between a pixel coordinate system and a world coordinate system can be realized without accurate camera calibration, and the fusion precision of visual data and AIS data is improved. AIS and visual information are matched through a K-D tree algorithm, AIS data are projected to visual coordinates, and accurate matching of heterogeneous data is achieved. The system is provided with ship illegal behavior judgment and collision early warning rules, behaviors such as overspeed and abnormal AIS closing can be recognized, and early warning is triggered. Experiments prove that the method provided by the invention is strong in adaptability and high in robustness, and can effectively improve the ship monitoring and traffic management level of the bridge water area.

Systems and techniques are provided for a road intelligence network for improved road safety, traffic management, and / or driver monitoring. The road intelligence network can use monitoring information comprising image data of vehicles on a roadway, or can use monitoring information comprising relative position and distance information for vehicles on the roadway determined using a network of beacon transmitters and receivers. Real-time mapping information indicative of vehicle movement and trajectory can be determined from the monitoring information and correlated to specific vehicles to detect unsafe or undesired driving behaviors, which may be further based on comparisons with historical mapping information of the roadway or for particular vehicles, or baseline driving characteristics of neighboring vehicles. Remediation messages and actions can be transmitted to a mobile application on a user device of a driver, in response to detection of the unsafe driving behavior.

The invention relates to the field of artificial intelligence, particularly discloses an intelligent optimal management system for urban traffic congestion based on artificial intelligence, and aims to solve the problems of congestion and low efficiency caused by response delay, local optimization and low data utilization rate of an existing traffic management system. The system comprises a data acquisition and fusion module, a traffic state perception and prediction module, a decision optimization module, an instruction issuing and execution module and a man-machine interaction and visualization module. Through multi-source data fusion, graph neural network prediction and multi-agent reinforcement learning, traffic flow real-time perception, accurate prediction and adaptive control are realized, congestion is effectively relieved, and the overall operation efficiency and toughness of a road network are improved.

The invention provides a low-altitude airspace dynamic decision-making system based on a multi-modal large model, and relates to the technical field of low-altitude airspace traffic management.The low-altitude airspace dynamic decision-making system is characterized in that a first thermal disturbance coverage map, a second shear wind high-risk area map, a third turbulence risk drainage basin map and a fourth sudden obstacle prediction area map are generated by a map construction module; the aircraft can judge whether the aircraft enters the dangerous area in real time and assess the safety of the flight path according to the superposed risk value. When the superposition risk value # imgabs0 # of the flight path section passing through the jth area in the first original flight path of the ith aircraft is in a middle risk section, the system generates a first-level avoidance strategy, and the height, the speed and the course of the aircraft are adjusted; and when the superposed risk value # imgabs1 # is a high-risk section, the system generates a secondary avoidance strategy, and bypasses a high-risk area by remarkably increasing the flight height and reducing the speed.

The invention relates to a large-scale road network traffic control method based on a deep reinforcement learning large model, and belongs to the technical field of intelligent traffic control. The method comprises the following steps: sensing real-time multi-modal road network information including urban road intersections, highway entrance ramps and emergency lanes, and generating a space-time fusion representation vector representing a current traffic network state by fusing a space diagram construction method and a time sequence embedding method; the space-time fusion representation vector and historical state memory are spliced to serve as input, a backbone network of a pre-training large language model is used for state feature distillation so as to enhance state representation, and a traffic control decision is output through a strategy network with a layered action space; through cross-modal knowledge migration and a progressive course learning strategy, a training process of a deep reinforcement learning algorithm is guided and optimized so as to improve model training efficiency and generalization ability. According to the method, the generalization performance and the accuracy of the control strategy are improved while the real-time response speed is ensured.

Disclosed embodiments generally relate to edge-based multi-Radio Access Technology (RAT) traffic management (TM) solutions to support delay-sensitive traffic over heterogeneous networks. Embodiments include delay-aware TM implementations that split and / or steer network traffic across different RATs for the edge network control plane. Embodiments also include utilization threshold-based implementations to achieve delay-aware multi-path TM at the network's edge. The multi-path TM includes multi-RAT, multi-access, or multi-connectivity traffic routes. Embodiments include strategies to sort users (or devices) for making multi-RAT traffic distribution decisions and to determinate the utilization thresholds. Embodiments also include message exchange mechanisms or learning utilization thresholds and other useful system properties. Other embodiments may be described and / or claimed.

The invention discloses an urban road traffic entrance and exit influence evaluation method based on big data, and relates to the technical field of urban traffic management. Constructing an urban road traffic network topological graph based on the traffic feature vectors, proposing a dynamic weight graph embedding algorithm, and establishing a road network association mapping model; applying a graph neural network algorithm based on an attention mechanism to the road network association mapping model, performing road node influence factor evaluation, and quantitatively analyzing the road entrance and exit influence degree; fusing influence factor evaluation results, and constructing a multi-dimensional traffic influence evaluation model by adopting a cross-domain ensemble learning method; and according to a performance evaluation result of the multi-dimensional traffic influence evaluation model, generating urban road traffic entrance and exit optimization decision suggestions through an intelligent recommendation algorithm, and completing accurate scheduling of traffic network nodes. The intelligent recommendation algorithm is developed based on reinforcement learning, and reliable optimization suggestions are provided for traffic management decisions.

The invention relates to a city-level path guidance method and system based on dynamic clustering in a vehicle-road cloud cooperation scene, and the method comprises the steps: constructing a vehicle-road cloud cooperation architecture, and collecting traffic data; the traffic management center updates the road weight and initializes a penalty matrix, and performs congestion detection at the same time; when a congested road section is detected, vehicles possibly affected by congestion are screened out, and a to-be-planned vehicle set is formed; performing spatial clustering on the to-be-planned vehicle set to obtain a plurality of vehicle clusters, and constructing an independent path planning sub-graph for each vehicle cluster; distributing priorities for the vehicles in each vehicle cluster; planning an alternative path for the vehicle; performing batch dynamic adjustment on the road weight and the penalty value on the path planning subgraph; the traffic management center issues the planned alternative path to the corresponding vehicle through the road side unit, and the vehicle runs according to the new running path to realize path induction; finally, reasonable distribution of traffic flow, improvement of road network traffic efficiency and balanced allocation of time-space resources are realized.

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention discloses an unmanned aerial vehicle non-visual flight management method based on an unmanned area three-dimensional model, and belongs to the technical field of unmanned aerial vehicle air traffic management and autonomous flight control, and the method comprises the steps: fusing multi-source heterogeneous data to generate a probability map of a data confidence field; calculating a space-time risk potential field based on the map and the kinematics state of the unmanned aerial vehicle; performing adaptive perception and route optimization planning according to the risk potential field; and a closed-loop feedback signal is generated according to the flight execution deviation so as to regulate and control the data fusion and risk calculation process, and data assimilation is carried out. According to the technical scheme, environment modeling fusing multi-source data and confidence evaluation is adopted, dynamic risk calculation of kinematics and global closed-loop feedback regulation are combined, and the safety, autonomy and environment adaptability of non-visual flight of an unmanned aerial vehicle in a complex unmanned area can be remarkably improved.

The invention discloses an intelligent traffic data processing method and system based on a 5G communication system, and relates to the technical field of traffic data processing, and the method comprises the steps: collecting data based on a 5G communication network; preprocessing the collected traffic data through an edge computing node; real-time prediction and risk assessment are carried out on traffic flow based on the cloud platform; sending real-time traffic information and optimization suggestions to a traffic management center and related vehicles; when an emergency occurs, a rapid response mechanism is triggered through a 5G network, and traffic resources are scheduled. The system collects, processes and analyzes traffic data in real time through the characteristics of high bandwidth and low delay of the 5G communication network and the distributed processing capability of edge calculation, achieves the real-time prediction and management of traffic flow and accident risks, and can improve the road utilization efficiency, reduce traffic accidents and improve the traffic safety through the intelligent decision and optimization control strategy. And the emergency response capability is improved, so that a more intelligent solution is provided for urban traffic management.

The invention discloses a metro station non-commuting passenger proportion calculation method based on a random forest model, and the method specifically comprises the steps: selecting a research region, and collecting metro-related built environment data and transportation data; important built-up environment variables are determined through preliminary analysis, and variables influencing non-commuter are added; establishing a prediction model by adopting a random forest algorithm, and dividing a training set and a test set; analyzing the output of the random forest model by applying an SHAP technology, and determining the importance sequence of the influence of each built-up environment factor on the proportion of non-commuting passengers and the action mechanism of the influence of each built-up environment factor; according to the size of the SHAP value and in combination with an actual scene, a numerical matrix of all built-up environment factors around the subway station in the scene is determined; and substituting a model coefficient result into the established environment factor matrix, and calculating the subway station non-commuting travel proportion. According to the method, the prediction precision of the non-commuting passenger quantity of the subway station is remarkably improved, the understanding of non-commuting travel behaviors is deepened, and powerful support is provided for urban traffic management and planning.

The invention discloses an Internet of Vehicles traffic management method based on Beidou positioning, and belongs to the field of traffic management, and the method comprises the steps: obtaining Beidou positioning data and sensor network local sensing data, and extracting global coordinate information and local traffic state information; performing time synchronization processing on the Beidou positioning data and the sampling data of the sensor network by adopting a timestamp alignment method, and eliminating the difference in time scale; whether signal interruption or drifting exists in the Beidou positioning data is judged, and if signal abnormity exists, missing positioning data is estimated through an interpolation algorithm according to local sensing data of the sensor network; according to the fused data, constructing a dynamic distribution model of vehicles in a global road network, and identifying a relationship between traffic density and road network distribution; and a multi-source data regression model based on a convolutional neural network is adopted to predict the vehicle position during signal interruption or drifting, and continuous global positioning information is generated.

The invention discloses a micro-service full-link gray level traffic management system and method based on PaaS, and relates to the field of traffic management, and the system comprises an intelligent traffic analysis and decision center module, a micro-service arrangement and management platform, a full-link data analysis module, a dynamic safety protection and compliance auditing module, and a distributed configuration center. According to the micro-service full-link gray flow management system and method based on the PaaS, dynamic self-adaptive gray strategy formulation can be carried out, the latest service target, the gray version release plan and the system performance index threshold can be synchronously obtained in cooperation with a distributed configuration center, and the flow management efficiency is improved based on the information and the intelligent screening result of the flow. A routing strategy and a version switching strategy of gray traffic can be dynamically generated, and in the strategy making process, the real-time load condition of a system, the availability of micro-services and key index changes of services are fully considered.

The invention relates to the technical field of traffic management, in particular to a road traffic dispersion and emergency command system construction method based on multi-source data fusion, and the method comprises the steps: collecting traffic flow sensor data, video monitoring data, meteorological data, social media user feedback data and the like through a multi-source data collection module; a data fusion and processing module is used, and data-level, information-level and decision-level fusion algorithms are adopted to carry out standardization processing, key information extraction and comprehensive evaluation and prediction on multi-source data. And constructing a traffic flow prediction model based on machine learning, such as a long and short-term memory network model, and accurately predicting the traffic flow. And according to the prediction result and the real-time traffic condition, a traffic dispersion strategy making module is used for adjusting signal lamp timing, issuing traffic guidance information and allocating traffic police resources. In the aspect of emergency command, multi-source data are integrated to evaluate event severity, deploy rescue resources and determine an optimal rescue route, and multi-department information sharing and cooperative work are achieved.

The invention discloses a road environment monitoring terminal based on the Internet of Things, and relates to the technical field of traffic environment monitoring. The road environment monitoring terminal based on the Internet of Things comprises an environment monitoring module, an edge computing module, an Internet of Things communication module, a cloud analysis module, an intelligent linkage module, a power supply module and an equipment self-maintenance module. According to the invention, real-time preprocessing and intelligent analysis are carried out on the environmental data through the edge calculation module, abnormal modes in the environmental data are rapidly identified, and a real-time analysis result is generated, so that the data transmission bandwidth demand and the processing delay are greatly reduced; the cloud analysis module combines historical data and real-time data, and provides a global evaluation result based on a deep learning model, thereby supporting the intelligence of traffic management decisions.

The invention provides an expressway variable speed limit control method, system and device and a medium, and belongs to the technical field of intelligent traffic management. The method comprises the following steps: collecting traffic flow data, road condition data and weather data in real time; preprocessing the collected data, and predicting a traffic jam trend and a safety risk index through a machine learning algorithm; determining weight factors of traffic flow, safety risk and congestion prediction through an analytic hierarchy process; dividing a road section speed range by using a clustering algorithm, and calculating a road section unit speed; constructing a target function which aims at maximizing the unit speed of the road section and minimizing the safety risk and the congestion time; and based on the target function, solving an optimal speed limit value by adopting a particle swarm optimization algorithm, and dynamically issuing speed limit information in different lanes. The highway traffic big data is analyzed in real time, the speed limiting strategy is intelligently formulated, the speed limiting strategy is published to the driver in real time through multiple channels, and the passing efficiency and safety of the highway are effectively improved.