348 results about "Traffic congestion" patented technology

The invention discloses an intelligent traffic control system and method based on multi-agent near-end strategy optimization, and belongs to the field of intelligent traffic, Internet of Vehicles and deep reinforcement learning. The method comprises the following steps: firstly, constructing a fog-cloud collaborative three-layer architecture, and realizing real-time monitoring and dynamic regulation and control of traffic flow through cloud global decision and local sensing collaboration of a road side unit (RSU); secondly, designing indexes of'road section overlap ratio 'and'road section time overlap ratio', and solving the problem of secondary congestion caused by rerouting; then, a multi-agent near-end strategy optimization (MAPPO) algorithm is adopted, so that the traffic signal lamp is used as an autonomous agent to dynamically adjust the phase, and the limitation of single-point control is broken; and finally, through integrated optimization of rerouting and adaptive signal control, an original multi-objective optimization problem is converted into a layered multi-agent reinforcement learning problem. According to the invention, vehicle driving time and system energy consumption can be effectively reduced, road traffic efficiency is improved, and active avoidance and dynamic alleviation of urban traffic congestion are realized.

The invention provides a traffic abnormity and congestion cause analysis method and system based on a knowledge graph, and relates to the technical field of intelligent traffic perception, and the method comprises the steps: carrying out the target detection through employing preprocessed laser radar point cloud data, recognizing traffic participants, carrying out the continuous frame tracking of the traffic participants, and extracting the motion track and behavior characteristics in an event dimension; identifying a behavior event of the traffic target based on the motion trail and the behavior characteristics, binding the identified behavior event of the traffic target to a corresponding target entity, and performing target behavior modeling to form standardized structured information; based on structured information of a traffic target, an intersection-oriented traffic state knowledge graph is constructed, a rule-based abnormal event judgment module is utilized to perform semantic analysis on behavior and event nodes in the traffic state knowledge graph, abnormal traffic events are identified, and potential causes causing traffic congestion are traced. According to the invention, refined understanding and active perception of the intersection traffic state can be realized.

The invention relates to a multi-type emergency vehicle signal dynamic priority control method based on vehicle-road cloud cooperation, and belongs to the technical field of intelligent traffic control. The method comprises the following steps: acquiring an emergency vehicle state and traffic environment data in real time through cooperation of a vehicle-mounted terminal, roadside equipment and a cloud control platform; the cloud control platform dynamically calculates priority scores based on vehicle types, task emergency degrees, predicted arrival time, real-time traffic influences and path complexity multi-dimensional factors; when multiple vehicles have conflicts, collaborative decision making is carried out based on scores; and finally, an optimized signal control strategy is generated and executed. The system effectively solves the problems that a traditional priority control mode is extensive, and traffic jam and multi-vehicle conflicts are easily caused, achieves the purpose that interference to social traffic is minimized while efficient passing of emergency vehicles is guaranteed, and improves the overall efficiency and safety of an urban traffic system.

The invention relates to the technical field of warehouse management, in particular to an intelligent warehouse management system based on deep learning, which constructs a space-time diagram model comprising nodes and edges by combining historical warehouse-in and warehouse-out records, AGV (Automatic Guided Vehicle) tracks and scheduling logs on the basis of spatial distribution information and channel communication relations of warehouse goods locations and function bits. And through deep space-time diagram convolution modeling, outputting a goods allocation value score and a traffic congestion probability, and identifying a goods allocation area which should be preferentially allocated by the high-frequency SKU. And a goods allocation strategy is generated through a reinforcement learning model, and excessive occupation and deadlock of channels are avoided while high-frequency goods are quickly put in and put out of a warehouse. And finally, generating an AGV scheduling instruction set through a path planning algorithm, and issuing and executing the AGV scheduling instruction set. Dynamic collaborative optimization of goods allocation planning and AGV scheduling is realized, the high-frequency goods operation efficiency is improved, meanwhile, path congestion and conflicts are effectively avoided, and the throughput rate and the operation reliability of the system are improved.

The invention relates to the technical field of path planning, in particular to a multi-agent dynamic nuclear emergency evacuation path planning method and system based on deep reinforcement learning. In particular to a multi-agent optimization path generation and dynamic adjustment method based on a deep reinforcement learning technology, and aims to improve the real-time performance, the safety and the execution efficiency of a public evacuation path in an emergency nuclear event. According to the scheme, the optimal evacuation path is automatically explored through deep reinforcement learning, a path model does not need to be manually preset, complex factors such as pollution dynamic diffusion and traffic jam under the nuclear accident scene can be dealt with, and the efficiency and safety of emergency evacuation path planning are greatly improved. The system is suitable for public emergency response systems in high-risk environments such as nuclear accidents, earthquakes, fire disasters and urban explosions.

The invention discloses a parking lot virtual green wave guiding method and related equipment. The core is to construct a space-time resource map, plan a path according to the space-time resource map, reserve a time window to form a virtual green wave band, issue an instruction to a vehicle terminal and an intelligent barrier gate, monitor vehicle position deviation, and trigger local re-planning if a threshold value is exceeded. According to the scheme, the defects in the prior art are accurately overcome, global space-time resource overall planning is achieved through a space-time resource map, an exclusive time window is reserved for vehicles, multi-vehicle path conflicts are eliminated from the source, and the defects of lack of global view and active scheduling are overcome; static road network navigation is replaced by a space and time coordinated virtual green wave band, vehicles are actively guided to pass orderly, microscopic traffic congestion is effectively solved, and the in-field passing efficiency is greatly improved; through a real-time monitoring and local re-planning mechanism, emergency situations such as vehicle faults can be rapidly handled, congestion diffusion is avoided, traffic flow stability is guaranteed, and finally seamless and non-stop passing of the vehicle from an entrance to a parking space is achieved.

The invention discloses an urban elevated expressway congestion relieving method and system based on detector data. The method comprises the steps that multi-dimensional traffic parameters of key sections of an elevated expressway are collected in real time through arranged traffic detectors; establishing a multi-level threshold matrix based on the multi-dimensional traffic parameters, and dynamically dividing traffic congestion levels; when the real-time traffic parameters meet the threshold condition of any congestion level, a disposal instruction of the corresponding level is automatically triggered; according to the processing instruction, executing a multi-device cooperative control strategy covering a congestion point space correlation area; and dynamically optimizing the threshold matrix or the cooperative control strategy according to traffic parameter change feedback after strategy execution. According to the invention, by monitoring the traffic data in real time, dynamically setting the congestion threshold and automatically matching the optimal mitigation strategy, rapid early warning and accurate intervention of traffic congestion can be realized, and the traffic efficiency and intelligent management level of the elevated expressway are improved.

The invention belongs to the field of intelligent traffic systems, and particularly relates to a congestion scene emergency decision-making method based on dynamic traffic environment modeling, which comprises the following steps: by introducing a congestion potential field concept, fusing multiple factors such as traffic density, speed, cart proportion and the like into a quantifiable risk index, and distinguishing a current congestion index from a future congestion index; the layout problem of the monitoring points is converted into a mathematical optimization problem with the aim of improving the decision effect from experience design; in the decision-making process, not only is passing efficiency considered, but also dimensions such as safety, user experience and resource utilization rate are included; according to the closed-loop intelligent management system integrating traffic flow long-time-sequence evolution prediction, congestion risk assessment based on the physical potential field theory, multi-dimensional comprehensive decision and monitoring point layout collaborative optimization, the opening and closing opportunity of an emergency lane is scientifically and prospectively determined, and the layout of sensing equipment is synchronously optimized; the traffic jam is relieved to the maximum extent, the road passing efficiency and safety are improved, and meanwhile energy consumption is reduced.

The invention discloses a multi-mode urban traffic prediction system, belongs to the technical field of traffic, and solves the problems that a tunnel structure is not early warned in time due to hidden damage under the action of multiple coupling, and finally, a river-crossing tunnel bursts water suddenly and is forced to be closed under the triggering of peak traffic flow vibration, so that the tunnel structure cannot be early warned. Therefore, the problem of chain paralysis of the traffic system of the whole city is solved. Comprising a multi-modal data acquisition module, a damage evolution modeling module, a traffic influence analysis module, a collaborative optimization control module and a dynamic plan generation module. According to the method, the sensing capability is constructed by fusing multi-source monitoring data, hidden structure damage is identified and predicted by means of a multi-physics field coupling model, a traffic collaborative optimization strategy is generated based on adaptive dynamic planning, and then whole-process prevention and control from risk to emergency are realized through a dynamic plan and meta-learning. Therefore, the vicious circle of structural damage-traffic jam-rescue blocking is blocked, and regional paralysis is avoided.

The invention discloses a mixed traffic flow-oriented automatic driving taxi dynamic scheduling system and a collaborative decision-making method, belongs to the field of intelligent traffic systems, and solves the problem that in the coexistence environment of manual driving vehicles and automatic driving taxies, the automatic driving taxies cannot be automatically scheduled. The technical problem of how to efficiently and cooperatively dispatch vehicles, accurately predict demands, optimize energy management and improve the overall operation efficiency of the system is solved. The system comprises a scheduling server which is connected with a road side sensing unit, a vehicle-mounted control unit and a charging station management platform. The scheduling server comprises a traffic flow analysis module; a demand prediction module; a dynamic scheduling module; and an energy collaboration module. The system is mainly used for realizing real-time, dynamic and intelligent scheduling and energy management of the automatic driving taxis in the mixed traffic flow, improving the operation efficiency, relieving the traffic jam and optimizing the charging resource utilization.

The invention discloses a traffic drainage signal management method and system based on a Beidou system, and relates to the technical field of Beidou satellite navigation, and the method comprises the steps: obtaining the real-time traffic data of the position and speed of a vehicle at each intersection based on the Beidou system, and transmitting the real-time traffic data to a traffic management platform; carrying out feature matching on the accident identification model trained based on historical emergency data and a deep learning algorithm and real-time traffic data, and identifying an emergency; and constructing a real-time traffic situation map in combination with the identified emergency information and the traffic condition information of the intersections. The vehicle position and speed data are obtained in real time through the Beidou system, emergencies are rapidly recognized and signal lamp timing is dynamically adjusted in combination with a deep learning algorithm, and compared with a traditional fixed timing scheme, traffic flow changes can be responded in real time, frequent start and stop of vehicles are reduced, the road passing efficiency is improved, and the traffic safety is improved. And particularly, in a sudden traffic accident or construction scene, a signal strategy can be quickly optimized, and traffic congestion aggravation is avoided.

The invention discloses an electric vehicle man-machine cooperative scheduling strategy for multiple scenes of an electric power traffic coupling network, and aims to solve the problem of electric vehicle charging optimization scheduling caused by deep coupling of an electric power system and a traffic system in different scenes. The method comprises the steps that firstly, topological information and behavior characteristics are fused through a graph generative adversarial network, a graph structured model is constructed, and an electric power traffic coupling operation scene is generated; secondly, establishing a multi-objective optimization mechanism by utilizing hierarchical reinforcement learning, constructing an electric vehicle charging optimization scheduling finite Markov decision model in a conventional scene and a fault scene, and designing an algorithm based on knowledge distillation to solve a scheduling strategy; and finally, realizing strategy migration of charging redistribution and path emergency adjustment in a fault scene by combining a man-machine cooperative regulation and control technology and fusing a user instruction. Experimental results show that the strategy can effectively improve the toughness of the power grid, relieve traffic congestion, reduce charging queuing time and increase user satisfaction.

The invention relates to the technical field of intelligent tourism management, and discloses a tourist behavior dynamic modeling method based on space-time big data, which comprises the steps of collecting and preprocessing multi-source positioning data, inputting the multi-source positioning data into a multi-source positioning fusion engine, and combining statistical optimization and deep learning model fusion to obtain a high-precision positioning model. Track reconstruction and time sequence aggregation are carried out on continuous position points of tourists based on a high-precision positioning model, a structured activity data graph is generated, activity data of the tourists at all positions are obtained, activity preference characteristics are extracted through an association rule mining and clustering algorithm, and a Markov chain and a spatio-temporal evolution model are combined to predict a tourist flow trend. And forming a flow prediction result, and generating a visual dynamic decision support by using the prediction result. According to the invention, visual and dynamic decision support is provided, the crowd congestion is relieved, and the operation safety of the scenic area is guaranteed.

The invention discloses an optimized operation method and system for a wind-solar storage micro-grid. According to the method, a three-layer architecture of top-layer optimization, middle-layer cooperation and bottom-layer execution is adopted, and an intelligent closed loop of'prediction-planning-coordination-execution-feedback 'is formed. The top layer carries out global power flow congestion identification and dispersion based on LSTM prediction and a traffic congestion algorithm, carries out multi-target refined optimization in a feasible interval by using an improved particle swarm algorithm, and generates an optimal operation instruction; the middle layer dynamically adjusts and optimizes the target weight according to the operation scene, and decomposes an instruction into a multi-device cooperation strategy; and the bottom layer executes a customized control algorithm to realize accurate regulation and control and millisecond-level rapid protection of the equipment. The problems of micro-grid multi-target collaborative optimization, predictive control disjunction, operation risk look-ahead protection and the like are effectively solved, and the voltage stability, economical efficiency, dynamic response speed, safety and reliability of system operation are remarkably improved.

The invention provides a traffic jam prediction method and system based on big data analysis, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous traffic data of a preset region, and carrying out the fusion of the processed multi-source heterogeneous traffic data, and obtaining the fusion traffic data; the fused traffic data comprises dynamic traffic flow data, static road network topology data and external environment data; carrying out feature extraction on the fused traffic data to obtain a multi-dimensional traffic feature vector containing space-time dimensions and external factors; based on the multi-dimensional traffic feature vectors, a space-time diagram diffusion convolutional network is adopted to construct a base prediction model; and introducing a meta-learning framework, and combining with the base prediction model to obtain a traffic jam prediction model so as to complete real-time prediction of the traffic jam condition of the to-be-predicted area. According to the model constructed by the invention, the parameters of the model can be quickly adjusted by using a very small amount of initial real-time data, accurate capture and prediction of an abnormal congestion mode are realized, and the robustness and practicability of the system in a real and complex environment are greatly improved.

The invention discloses a traffic jam prediction method and device based on comparative learning and knowledge distillation. The method belongs to the technical field of traffic jam prediction. Comprising the following steps: S1, preprocessing an original traffic data set to obtain traffic characteristic time series data; s2, constructing a traffic jam prediction model; s3, constructing a multi-task loss function; s4, training a traffic jam prediction model by using the traffic characteristic time series data, and optimizing model parameters by minimizing a multi-task loss function; and S5, inputting a to-be-predicted traffic data set into the trained traffic congestion prediction model, and outputting a predicted congestion level of each node. According to the invention, the comparison learning module is adopted to maximize the hidden space difference between the samples so as to enhance the robustness of the features, the problem that the generation mode of a traditional variational auto-encoder is single is relieved, and the prediction precision in a complex scene is improved.

The invention discloses a traffic jam detection and early warning method based on an unmanned aerial vehicle video stream, and belongs to the technical field of intelligent traffic. The system is combined with a floating vehicle triggering model, collects road videos in real time through an unmanned aerial vehicle, and realizes high-precision vehicle detection and tracking in combination with improved YOLOv8 and DeepSort algorithms; unmanned aerial vehicle data and floating vehicle GPS data are fused, three elements of traffic flow are quantified, and a dual-mechanism congestion detection model is constructed; respectively utilizing a GRU cooperative traffic wave theory to predict the congestion dissipation duration and utilizing an improved GRU + GCN model to predict the traffic situation; the signal lamps are dynamically regulated and controlled through multi-agent reinforcement learning, and active congestion relieving is achieved. The method has the advantages of wide-area coverage, high-precision perception and intelligent response, and the urban traffic control efficiency is remarkably improved.

The application discloses a highway traffic monitoring system based on unmanned aerial vehicle aerial photography, which comprises a camera arranged on a holder, the holder being arranged on an unmanned aerial vehicle, the unmanned aerial vehicle flying at a low speed above a designated highway lane, and the unmanned aerial vehicle driving towards vehicles on the highway lane; the camera is used for photographing a road surface image of the designated highway lane based on the unmanned aerial vehicle and sending the road surface image to an image recognition unit; the image recognition unit recognizes the vehicles in the road surface image; and a road surface monitoring unit determines whether traffic congestion occurs on the designated highway lane based on the positions of the vehicles in adjacent frames.

The invention discloses a fusion analysis and quality evaluation method based on multi-source data elements, and relates to the technical field of traffic data processing. In order to solve the problems that traditional traffic multi-source data fusion is weak in space-time relevance and quality evaluation is not combined with traffic scene characteristics, the method comprises the following steps: firstly, constructing a traffic multi-source data space-time alignment model to realize space-time unification of road condition images, vehicle GPS tracks, traffic signal lamp states and citizen reported data; secondly, proposing a dynamic weight fusion algorithm based on traffic congestion association degree, and dynamically adjusting the weight according to the association strength of data and congestion assessment; and finally, a multi-dimensional quality evaluation system containing traffic scene exclusive dimensions is established, quality dynamic tracking and early warning are realized in combination with a traffic flow time sequence prediction model, and according to the method, in a smart city traffic scene, the traffic jam prediction accuracy and the data quality evaluation and traffic decision adaptation degree are improved, and the method is significantly superior to a traditional method.

The invention belongs to the technical field of data processing, and particularly relates to an intelligent scheduling method and system for garbage transfer trucks, and the method comprises the steps: carrying out the time-space alignment and quantification processing of multi-source data, constructing a feature vector, and dynamically generating a transfer task set with a priority through a pollution prediction model; constructing a dynamic comprehensive transit time model fusing real-time traffic congestion, a predicted traffic trend and a vehicle operation mode; an improved genetic algorithm is adopted, the dynamic passing time is used as a core index to evaluate the path fitness, and path optimization is carried out; and smoothing the optimized path and issuing a control instruction. According to the invention, dynamic time is changed into a decision-making core of the introduced path planning, so that a real optimal path for intelligently avoiding traffic congestion can be planned, and the working efficiency of the garbage transfer vehicle and the adaptability to a dynamic urban environment are remarkably improved.

The invention discloses an event detection method and system based on a cross-regional global trajectory space-time diagram, and the method comprises the steps: firstly obtaining a video stream in each road monitoring scene, and then carrying out the multi-target tracking based on the video stream to generate a vehicle trajectory diagram in each road monitoring scene; carrying out vehicle matching identification on the vehicle tracks in each adjacent road inspection scene, splicing the vehicle tracks of the same vehicle in different road monitoring scenes, and constructing a global track space-time diagram; and finally, a fault event is identified based on the global trajectory space-time diagram, and the slope of the vehicle trajectory in the global trajectory space-time diagram is used for representing the vehicle speed which is used for reflecting the overall driving speed change of the vehicle in a plurality of road monitoring scenes. According to the technical scheme, typical events such as traffic jam, overspeed and illegal parking can be detected and analyzed, and the accuracy and response speed of traffic event detection are effectively improved through accurate positioning and state tracking.

The invention relates to the technical field of vehicle-road cooperative communication, and discloses a vehicle-road cooperative communication emergency response management method and system, and the method comprises the steps: recognizing micro-friction abnormal events through monitoring vehicle operation data, carrying out the time-space aggregation of the micro-friction abnormal events, and deducing the abnormal risk of regional road surface friction force. And generating high-priority early warning information and an emergency resource scheduling strategy according to the risk. The method effectively solves the problems of emergency response lagging and resource mismatching caused by difficulty in identifying the deep relevance of a large amount of fragmented and low-severity-level alarm information when the existing vehicle-road cooperation system faces the large amount of fragmented and low-severity-level alarm information. According to the invention, potential large-scale accident risks caused by hidden environmental factors such as black ice can be timely and accurately identified, so that high-priority early warning can be sent out at the early stage of an accident, targeted emergency resource scheduling is started, the expansion of the accident scale and the aggravation of traffic congestion are effectively avoided, and the accident safety is improved. And the traffic safety and the emergency response efficiency are obviously improved.

The invention provides a traffic dynamic cooperative control method and system based on multi-source heterogeneous data fusion, and relates to the technical field of traffic control, and the method comprises the steps: obtaining a multi-source heterogeneous data flow of a management and control region through a ubiquitous perception terminal cluster, carrying out the fine-grained data fusion, and outputting a space-time fusion state matrix; performing blind area state completion to generate a panoramic traffic state map; performing dynamic demand prediction based on the space-time deep learning model, and outputting a pressure distribution matrix; dynamically delimiting a traffic control boundary; performing multi-agent collaborative decision, and outputting a collaborative strategy packet; and according to the real-time dissipation rate deviation, performing closed-loop updating of the traffic coordination strategy. The technical problems that traffic data in the prior art mainly depends on a single data source, data missing or coverage blind areas are prone to occurring, real traffic conditions cannot be comprehensively reflected, traffic flow prediction errors and bottleneck prediction errors are caused, and finally traffic congestion is aggravated are solved.

A computer implemented method analyzes traffic. A processor set receives traffic images from Internet of Things devices in real time. The processor set identifies a choke point of traffic congestion using the traffic images. The processor set generates a map of the traffic congestion with real time updates, wherein the map includes a visualization of the traffic congestion at a number of distances relative to the choke point of the traffic congestion. The processor set sends the map of the traffic congestion with the real time updates including at least one of a stage of the traffic jam or resolution information for the traffic jam to a navigation computing device.

The invention discloses a self-adaptive control method and platform for an intelligent traffic signal lamp, and relates to the technical field of intelligent control, and the method comprises the steps: collecting traffic flow data in real time through a multi-source sensing module, including the number of vehicles, the vehicle speed, the vehicle type and the pedestrian flow; according to the data, traffic condition prediction is carried out by using a traffic flow model, and traffic pressure values of roads in all directions are calculated and generated; and based on the traffic pressure value, determining the phase duration of the signal lamp in each direction, performing signal lamp control analysis, generating a corresponding signal control instruction, and executing adaptive control of the traffic signal lamp. The technical problems of low traffic efficiency and serious traffic jam caused by the fact that the existing traffic signal lamp control method cannot dynamically adjust the time length of the signal lamp according to the real-time traffic condition are solved, and the purposes of dynamically optimizing the phase time length of the signal lamp and giving consideration to the requirements of pedestrians are achieved. The traffic efficiency is improved; and the congestion is relieved.

An AR signage display device interlocked with a vehicle, according to the present invention, may detect that a forward looking situation has occurred, on the basis of driving environment information of the vehicle and sensing data of the vehicle, and accordingly, carry out, for safe driving, display limitations on at least part of an AR digital signage included in a front image. Accordingly, on the basis of the driving environment information, such as traffic congestion zones or reduced visibility of the vehicle, the driver's front view is not blocked, and a region related to the driving environment information is not covered.

The invention belongs to the field of intelligent traffic systems and green logistics energy management, and provides a method for optimizing an energy path of an electric vehicle logistics fleet under dynamic traffic, which comprises the following steps of: defining an energy path optimization problem of the electric vehicle logistics fleet under a dynamic traffic environment, the coupling influence of multiple factors such as traffic jam, energy consumption and charging is considered, and a corresponding mathematical model is constructed. And then, designing an optimization framework based on simulation DeepSeek style reasoning, and combining various advanced reasoning mechanisms, such as thinking tree, self-consistency and multi-agent dialogue, to realize dynamic collaborative optimization of charging and paths in the motorcade. And finally, a closed-loop optimization method is provided, and optimal allocation of energy and time is realized by simulating DeepSeek energy decision and later optimization refinement. According to the method, the energy consumption of the motorcade under dynamic traffic can be effectively reduced, and the distribution efficiency is improved.

The application provides a shared parking space automatic matching and guiding navigation method based on a passive Internet of Things label, relates to the technical field of intelligent traffic, and comprises the following steps: receiving user terminal information, constructing a candidate parking space set based on a multi-layer search grid and density clustering, determining an optimal navigation path by using a road network connection state matrix and a congestion propagation prediction tree, and combining a parking space group state propagation model to calculate a maximum reservation waiting time, so that parking space reservation and verification are realized. The application improves the parking space resource utilization rate, reduces the time for users to find parking spaces, and effectively alleviates the urban traffic congestion problem.

The present application relates to heavy load AGV cooperative control technical field, especially in a kind of heavy load AGV group's cooperative control method, the motion state parameter of each heavy load AGV is collected to construct motion state vector, local traffic congestion parameter is predicted, and then local congestion index is calculated and dynamic coordination trigger threshold is determined;Distributed speed coordination is triggered based on local congestion index and dynamic coordination trigger threshold, to determine the reference coordination speed of each heavy load AGV;The reference coordination speed is passed to relevant heavy load AGV, to generate transmission coordination speed;Based on ground state parameter, the motion state vector of each heavy load AGV and transmission coordination speed, the traffic optimization sorting of each heavy load AGV is determined, to modify transmission coordination speed and determine modified coordination speed;Based on the modified coordination speed of each heavy load AGV, the timing and number of subsequent local traffic congestion are predicted, to determine the effective duration of coordination control.The present application improves the running efficiency and safety of heavy load AGV group in complex traffic environment.

The invention relates to the technical field of traffic control optimization, and discloses a traffic jam control optimization algorithm based on a regional population scale and an economic structure. The algorithm comprises the following steps: firstly, obtaining population distribution data, economic industry data and real-time traffic flow original data of a target area, and generating an area traffic characteristic representative data set through multi-dimensional clustering processing; generating a traffic regulation and control allocation instruction set based on the data set; then constructing a dynamic traffic state hierarchical optimization model containing a top layer road network stability model and a bottom layer resource cost model; and finally, determining a cooperative constraint condition of the two models, inputting an instruction set into the models, performing cross-layer iteration updating solution according to the constraint condition, and outputting an optimal traffic control strategy configuration scheme. According to the algorithm, multi-dimensional data are fused, collaborative optimization of road network stability and resource cost is realized, the accuracy and adaptability of traffic jam treatment are improved, and regional traffic jam is effectively relieved.