667 results about "Traffic congestion" patented technology

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

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 discloses a cross-regional vehicle-mounted network switching method and system based on digital twinning, and a medium. The method comprises the following steps: constructing a cloud side end digital twinning network architecture; acquiring vehicle state data and traffic environment sensing data through the terminal sensing layer; carrying out data aggregation on the vehicle state data and the traffic environment perception data through edge computing nodes to obtain a local digital twinborn model; meteorological data, operator base station data and a local digital twinborn model are integrated through a cloud digital twinborn center to obtain a global digital twinborn model, and the traffic congestion condition and the network congestion condition of each road network region in a future time period are predicted according to the global digital twinborn model. Determining a network switching strategy according to the traffic congestion condition and the network congestion condition; and performing a network switching decision on the target vehicle through the edge computing node. According to the method, the timeliness and accuracy of cross-regional vehicle-mounted network switching are improved, so that the stability and reliability of the vehicle-mounted network are improved, and the method can be applied to the technical field of the Internet of Vehicles.

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 discloses an intelligent parking management system based on multi-dimensional data analysis, and relates to the technical field of urban parking management, and the system can dynamically monitor the real-time state of a parking lot through a parking lot information collection module, can obtain the parking demands of a driver through a to-be-parked person collection unit, and can achieve the intelligent parking management of the driver. Comprise distance preference, cost budget and parking space types, and the most suitable parking lot is accurately matched by combining static and dynamic characteristics of the parking lot, so that the parking convenience is improved. And the correction module calculates a parking cost performance index of the parking lot, and performs dynamic adjustment according to a cost performance threshold value Z, so that the recommended parking lot is ensured to meet requirements, cost, parking convenience and parking space stability are considered, and an optimal choice is provided for a user. The system can reduce disordered wandering of vehicles in high-demand time periods or hot spot areas, improves reasonable configuration of parking resources, reduces traffic congestion caused by searching parking spaces, and improves the overall efficiency of urban parking management.

The invention discloses a highway intelligent emergency management method and system, and relates to the technical field of intelligent traffic, and the key points of the technical scheme are that multi-source heterogeneous data are fused to construct a three-dimensional situation awareness library, and accurate positioning and prediction of traffic congestion are realized; constructing a virtual emergency scene on a simulation platform based on a Bayesian network, evaluating traffic management efficiency indexes of different management and control plans, and analyzing accident influence and rescue path feasibility; an optimization objective function is established to solve an optimal resource scheduling scheme, and the optimal resource scheduling scheme is issued to the intelligent interaction device in real time; the Bayesian network and the deep reinforcement learning model are dynamically optimized through equipment feedback data, and scheme self-adaptive adjustment is achieved; according to the scheme, factors such as traffic flow dynamic change, road topology and environment are comprehensively considered, quick response is facilitated when an emergency occurs, rescue time and traffic jam loss are reduced, and the overall level of highway emergency management is improved.

The invention discloses a high-density stereoscopic warehouse ex-warehouse scheduling and transportation system and method based on RGVs, and relates to the technical field of warehouse ex-warehouse scheduling and transportation, and the system comprises a scheduling management center which is in communication connection with a virtual model construction module, a task analysis and planning module, a task distribution module and a real-time monitoring intervention module. Wherein the modules are connected through electric signals; and the virtual model construction module is used for constructing a virtual model of the warehouse and the RGV. By constructing the virtual models of the warehouse and the RGVs and combining the digital twinning technology, the state of a physical entity can be synchronized in real time, the space-time paths of the multiple RGVs can be accurately simulated, path conflicts and traffic congestion are effectively avoided, the efficiency of warehouse-out operation is remarkably improved, task delay caused by the path conflicts is greatly reduced, and the working efficiency of warehouse-out operation is improved. And the accuracy and timeliness of warehouse-out operation are ensured, so that the overall logistics efficiency is improved.

The invention relates to the technical field of logistics distribution, and discloses a big data analysis method and system based on traffic transportation logistics, and the method comprises the steps: constructing a space-time diagram convolution network model, and predicting the traffic congestion propagation trend of an urban region; based on the congestion propagation trend, the order density and the distribution resource state, executing a space-time dynamic partitioning algorithm, and dividing the urban distribution region into a plurality of dynamic micro-regions; constructing a double-layer intelligent decision-making system, and generating a logistics fleet separation and combination decision and path planning; heterogeneous distribution resource dynamic marshalling is executed, and different types of distribution tools are combined into a miniature formation; using a multi-agent reinforcement learning algorithm to optimize the cooperation process in the micro formation; according to the invention, through fusion of congestion propagation prediction, dynamic micro-area division, double-layer intelligent decision and heterogeneous resource collaboration, key problems of traffic congestion, resource scheduling and'last kilometer 'distribution in urban logistics distribution are effectively solved.

The invention relates to the field of traffic management, and discloses a traffic situation prediction method based on multi-source heterogeneous data fusion, which comprises the following steps of: firstly, acquiring traffic situation related data of a target area from a plurality of data sources, including traffic flow data, vehicle speed data, video image data, meteorological data and historical traffic statistical data; secondly, preprocessing the acquired traffic situation related data, including data cleaning, normalization or standardization processing, and performing time-space synchronization and matching; wherein the data cleaning comprises noise removal, abnormal value processing and missing value filling; and finally, inputting the preprocessed data into a pre-trained traffic situation prediction model, and outputting the predicted traffic jam degree of the target area. According to the invention, comprehensive analysis is carried out through the traffic-related situation data and the emergency data, and finally, the purpose of improving the prediction comprehensiveness through a multi-source cooperation mechanism is achieved.

The invention provides a vehicle-road cloud collaborative mixed traffic flow optimization method, system and device, and relates to the technical field of intelligent traffic, and the method comprises the steps: extracting a mixed traffic flow feature set containing a road environment, a vehicle state and a driver behavior through obtaining and synchronously fusing the multi-source traffic data of a cloud end, a road end and a vehicle end; according to the method, the intention of a driver is predicted by using a double-layer LSTM model, a vehicle trajectory is predicted in combination with an RNN-LSTM model, confidence fusion analysis of the intention and the trajectory is performed on this basis, accurate prediction of traffic conflict events is realized, and global optimization of traffic flow is supported. According to the method, the intention recognition and trajectory prediction precision in a complex mixed traffic environment is effectively improved, the intelligent decision-making capability of a traffic management system is enhanced, and an efficient technical means is provided for relieving traffic congestion, improving the traffic efficiency and guaranteeing traffic safety.

The invention provides a lane control strategy optimization method and system for a traffic congestion state, and the method comprises the steps: firstly obtaining a real-time traffic state data set of a target road section, including vehicle flow, driving speed and road space occupancy data, carrying out the traffic congestion feature analysis of the real-time traffic state data set, and carrying out the traffic congestion feature analysis of the real-time traffic state data set; the method comprises the following steps: obtaining congestion area distribution, congestion degree evaluation and congestion cause correlation characteristics, generating an initial lane control strategy containing variable lane direction setting, lane opening and closing states and lane passing priority distribution parameters based on the characteristics, sending the initial strategy to road control equipment for execution, and collecting and feeding back a traffic state data set; and adjusting strategy parameters according to a comparative analysis result of the feedback traffic state data set and the real-time traffic state data set, and generating an optimized lane control strategy, thereby improving the adaptability of the lane control strategy, and effectively improving the road traffic efficiency and the congestion relieving capability.

The invention discloses an automatic business travel itinerary optimization method, and relates to the technical field of intelligent itinerary planning, and the method comprises the steps: integrating the multi-source heterogeneous data of enterprise policies, personal preferences and real-time traffic through a federated learning framework, and achieving the cross-domain knowledge sharing; the method comprises the following steps: constructing a staged optimization engine by adopting an attention mechanism to dynamically balance cost, time, comfort and sustainability targets: in the first stage, modularly disassembling a travel through sparse constraint linear programming, and quickly generating a Pareto frontier candidate set; in the secondary stage, on the basis of a multi-agent reinforcement learning framework, complex interaction is simulated through a Markov decision process, and strategy iteration is driven through a special reward function for quantifying a comfort index; in order to cope with real-time disturbance, event-driven edge computing nodes are deployed, flight delay and traffic jam emergencies are responded in real time, an incremental topology updating algorithm is triggered, and only affected sub-modules are reconstructed to reduce computing complexity. According to the invention, the bottleneck of dynamic adjustment efficiency and multi-target balance capability is solved.

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 the technical field of intelligent traffic control, in particular to a single-intersection signal lamp dynamic timing method and system based on holographic perception, and the method comprises the steps: collecting the information of a single intersection and surrounding traffic participants in real time through a multi-source holographic perception device, and carrying out the preprocessing, thereby obtaining a standardized traffic data set; based on the data set, combining vehicle, pedestrian and non-motor vehicle demand weights, constructing a traffic demand evaluation model, and calculating a demand priority coefficient of each entrance lane; constructing a signal lamp dynamic timing optimization objective function by taking minimization of intersection total vehicle waiting time, minimization of pedestrian crossing waiting time and maximization of intersection traffic volume per unit time as objectives according to the priority coefficient in combination with intersection traffic capacity constraint conditions, solving to obtain an optimal signal lamp timing parameter, and issuing the optimal signal lamp timing parameter to a control terminal; and traffic state feedback data of the intersection are collected in real time, and timing optimization is carried out again if the change exceeds a preset threshold value. The system can wait for a long time, improves the intersection passing efficiency, and relieves traffic congestion.

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 provides a traffic control decision-making method, device and equipment based on data analysis, and aims to solve the technical problems of high subjectivity, insufficient data value mining, disjunction of strategy and practical application and lack of continuous learning optimization capability in the traditional traffic control decision-making. Through standardized fusion processing of multi-source traffic data and reverse optimization configuration of a traffic feature library, in combination with a progressive effect evaluation and reverse deduction verification mechanism, a multi-time scale effect tracking and strategy evolution trajectory acquisition system is innovatively established, and a decision cycle mechanism with autonomous learning and dynamic adjustment capabilities is constructed. The association relationship between the traffic state evolution rule and the control strategy is systematically analyzed, and finally an intelligent traffic control decision framework based on operation state data driving is formed; scientific and reliable decision support and technical basis are provided for application scenes such as urban traffic fine management, intelligent traffic system optimization control, traffic jam treatment and traffic safety guarantee.

The invention provides an electric vehicle charging resource planning and optimizing method, and aims to solve the problems of static performance, single target optimization, one-sided attraction evaluation, disjunction with traffic jam management and the like in existing planning. The method comprises the following steps: firstly, acquiring regional basic data, and establishing a charging station composite attraction evaluation model which comprehensively considers POI value, plot cost, estimated available charging pile quantity proportion and reputation parameters determined based on historical service data, so as to predict the probability of selecting each charging station by a user; and optimizing charging pile capacity configuration according to the predicted user selection probability and the total charging demand so as to minimize the combined cost of the construction cost and the average queuing waiting time of the users. The optimal charging site is screened and recommended through weighted aggregation of multiple indexes such as composite attraction, construction cost, queuing waiting time and the like. Scientific site selection and reasonable capacity configuration of the charging stations are achieved, urban traffic congestion is effectively relieved, balanced distribution of the charging flow is promoted, and the charging experience of users is improved.

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.

A vehicle display apparatus includes: a display that displays traveling information of a vehicle; an acquisition unit that acquires position information of the vehicle and traffic congestion information of an other vehicle that travels in a periphery of the vehicle; and a display controller that detects an occurrence of a traffic congestion from the position information and the traffic congestion information, and upon detecting the occurrence, displays, on the display, transition information related to transition to autonomous driving by using an image of at least one of a traveling road, the other vehicle, or the vehicle.

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 relates to the technical field of ETC special situation processing, in particular to an efficient ETC special situation processing system and a working method thereof, and the system comprises a special situation monitoring module which monitors the ETC lane transaction state in real time and obtains ETC lane special situation information; the special situation scene diagnosis module is used for carrying out special situation scene diagnosis analysis on the ETC lane special situation information to obtain special situation type information; the processing scheme matching module is used for substituting the special situation type information into a pre-constructed special situation processing scheme library for comparison to obtain a special situation processing scheme; the multi-modal guide execution module is used for guiding the user to complete special condition processing operation through visual and auditory dual channels based on the obtained special condition processing scheme, and obtaining a special condition processing result; the processing result verification module is used for verifying the special condition processing result in real time, and recording the special condition processing result in response to the fact that the special condition processing result is not abnormal; the parking time of vehicles at the toll station can be shortened, traffic jam is relieved, and the overall passing efficiency of the expressway is improved.

The invention relates to the technical field of safety management, in particular to a city safety emergency management system design scheme based on a three-dimensional map, and the scheme comprises a facility space construction module, a risk identification module, a disaster type area identification module, a linkage early warning marking module and an avoidance path analysis module. According to the invention, risk scoring is carried out on facility node coordinates, function levels, the number of accommodated people and service radiuses in a city three-dimensional map, the risk distribution condition of each facility node is visually reflected, and the node risk levels are displayed in a three-dimensional manner by setting the identification map layer through column height, color marking and flicker frequency. Visual focusing and spatial clustering of risk nodes are realized, a commander is helped to quickly identify a high-risk area, response grading of a disaster situation interaction area is effectively refined by performing composite situation grade evaluation on a composite disaster type area and marking an early warning icon, an avoidance passage path is automatically screened and planned, and traffic congestion and secondary risks are avoided in real time.

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 discloses an electric power, traffic and communication network collaborative electric power recovery method and system and a medium, and the method comprises the steps: inputting three-network comprehensive data of a target region into a deep classification-clustering model to obtain a three-network clustering result, and carrying out the calculation to generate a three-network node coupling tensor; determining a fault node set of the power network, and quantifying the cross-network influence of fault nodes by using a three-network node coupling tensor; based on the cross-network influence of the fault node, a chain risk value is obtained, and thus the weak level of the fault node is obtained; based on the weak grade of the fault node and the cross-network influence of the fault node on the traffic congestion rate and the communication delay, constructing a three-network cooperative power recovery model, and solving to obtain an optimal power recovery strategy; performing power recovery on the target area based on the power recovery optimal strategy; the power can be recovered through cooperation of power, traffic and the communication network, the recovery difficulty is small, the efficiency is high, and power failure economic losses and first-aid repair resource waste are reduced.

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 traffic jam state analysis method and system based on a dynamic pixel scale. The traffic jam state analysis method comprises the following steps: initializing and loading a model; acquiring a video frame and carrying out target detection; tracking multiple targets; traversing a tracking target and calculating the speed of a single vehicle; traversal of the tracking target and calculation of the single vehicle speed comprise the following steps of obtaining the current position, calculating the pixel displacement of the center point of the bounding box, dynamically estimating a pixel scale, calculating the real displacement and speed, outputting the smooth speed, and updating position information. Through a low-cost and high-precision video analysis method, on the premise that complex manual camera calibration is not needed, self-adaptive estimation of the vehicle speed on a road is achieved, and the vehicle congestion state is accurately judged in real time on the basis of the self-adaptive estimation.

The invention provides a multi-intersection coordinated traffic control synchronization method, device and equipment, and aims to solve the technical problems of inconsistent coordination timing among intersections, lack of a unified decision-making mechanism, fracture of coordination effect and insufficient fault recovery capability in traditional single-intersection independent control. By establishing an inter-intersection communication connection network and a shared data processing system, a weighted coordination framework and an elastic multi-point synchronous control mechanism based on priority distribution are innovatively constructed, and a self-adaptive reconstruction and efficiency-increasing optimization system is established in combination with coordination effect chain analysis and a flow relay adjustment strategy. Layered management of intersection coordination decision and effect relay transmission amplification are systematically realized, and finally a multi-intersection integrated control framework with unified coordination, elastic adjustment, self-adaptive fault tolerance and continuous synergy capabilities is formed. Scientific and reliable technical support and technical basis are provided for application scenes such as urban regional traffic coordinated management, intelligent traffic system multi-point control, traffic jam coordinated treatment, road network efficiency optimization and the like.

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.