875 results about "Traffic congestion" patented technology

The invention belongs to the technical field of intelligent traffic, and particularly relates to a commuting regular bus operation line intelligent early warning system, which comprises a data acquisition module, a data communication module, a processing analysis module, an early warning output module and a feedback module, and a commuting regular bus operation line intelligent early warning method comprises a risk prediction model training process, the specific process is as follows: step 1, a data preparation stage; 2, a model construction stage; 3, a model training stage; 4, a reinforcement learning optimization stage; 5, a verification and deployment stage; and step 6, a closed loop iteration stage. According to the invention, the limitation of a single data source can be broken through, the early warning precision is improved, and accidents such as sudden traffic congestion, vehicle faults, driver misoperation and dangerous behaviors of passengers can be timely handled; a processing feedback continuous iteration model is combined to adapt to a complex traffic scene; emergency resources are automatically matched based on the early warning level, and the operation loss is reduced.

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 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 invention discloses an expressway digital twinborn dynamic construction method, which relates to the technical field of traffic, acquires and preprocesses multi-dimensional data of an expressway system, and constructs a traffic flow model of an expressway based on a deep learning technology and in combination with the multi-dimensional data of the expressway system. The method comprises the steps of mapping multi-dimensional data, collected in real time, of an expressway system to a traffic flow model, dynamically updating a traffic state in the traffic flow model, performing short-term traffic flow prediction by using the traffic flow model, simulating influences when different traffic events occur, and analyzing existing risks. According to the method, the valuable traffic information is extracted from the mass data by capturing and analyzing the traffic flow data in real time and utilizing the machine learning and data mining technology, so that the digital twinborn body can accurately predict the future traffic flow and provide a decision-making basis, the occurrence of traffic congestion and accidents is reduced, and the traffic flow prediction efficiency is improved. And the traffic capacity and the safety of the expressway are improved.

The invention relates to the technical field of municipal road construction, and discloses a municipal road construction section scheduling optimization method and system based on artificial intelligence, and the method comprises the steps: collecting the multi-source dynamic data of a construction region through Internet of Things equipment and a satellite remote sensing technology, and constructing a time sequence database; a multi-objective optimization model is established, and an initial scheduling scheme is generated by adopting a non-dominated sorting genetic algorithm with the goal of minimizing construction period, resource waste and traffic jam influence. Dynamically adjusting the priority of the construction section by using a reinforcement learning algorithm, and updating a construction task sequence; a graph neural network is utilized to detect space-time conflicts of construction sections, and conflict-free scheduling constraint conditions are generated; and optimizing resource allocation based on mixed integer linear programming, simulating a construction process in combination with a digital twinborn technology, correcting a construction progress deviation by using a Kalman filtering algorithm, and outputting a final scheduling instruction. According to the invention, optimization of municipal road construction section scheduling is realized, the construction period is effectively shortened, resource waste is reduced, and traffic jam is relieved.

The invention relates to the technical field of traffic flow regulation and control, and discloses an electronic information traffic flow automatic regulation and control system based on a wireless sensor network. The multi-source sensing module collects road network multi-dimensional traffic data through a wireless sensing network, and traffic situation characteristics are generated through a specific data fusion method. The spatio-temporal feature fusion module utilizes a spatio-temporal attention mechanism to associate cross-modal features, and the collaborative decision-making module inputs fusion features into a pre-trained distributed decision-making model to generate a regulation and control instruction set. The dynamic game optimization module constructs a multi-target game optimization model, and traffic signal parameters are optimized by adopting a dynamic game strategy decomposition algorithm. And the hierarchical execution module executes regulation and control instructions in a distributed manner through a three-level control architecture of a central decision-making layer, a regional coordination layer and an intersection execution layer. The system can comprehensively collect and fuse traffic data, realizes scientific decision making and accurate regulation and control, effectively improves the road passing efficiency, balances the road network load, and relieves traffic jam.

The invention provides a traffic safety detection method and system based on big data analysis, and the method comprises the steps: obtaining a to-be-detected traffic internet-of-things data set, carrying out the fusion representation information mining of the to-be-detected traffic internet-of-things data set, and obtaining first fusion representation information; the first fusion representation information comprises multiple pieces of data dimension representation information corresponding to multiple data dimensions; indexing a plurality of historical traffic internet-of-things data sets matched with the traffic internet-of-things data set to be detected according to the first fusion representation information, and obtaining a traffic congestion level mark corresponding to each historical traffic internet-of-things data set according to second fusion representation information corresponding to each historical traffic internet-of-things data set and the traffic congestion level mark corresponding to each historical traffic internet-of-things data set; generating a traffic situation fusion template according to the first fusion representation information of the traffic internet-of-things data set to be detected; and according to the traffic situation fusion template, detecting a traffic congestion level mark of a traffic internet-of-things data set to be detected. According to the invention, the traffic jam grade marks can be efficiently and accurately obtained.

The invention relates to a time delay optimized satellite network adaptive routing method and system, and belongs to the technical field of satellite communication. The method comprises the steps of multi-hop information age construction, multi-path routing, link state sensing, flow segmentation adaptive routing, dynamic routing decision and the like. A multi-hop information age model is adopted to consider the time-varying property of a link state, the real-time property of path information is accurately estimated, and the timeliness of data updating is ensured; a multi-path search algorithm predicts link occupation frequency according to ephemeris information to construct a plurality of disjoint transmission paths, so that the traffic congestion problem is relieved, and the link resource utilization rate is optimized; a flow segmentation adaptive routing algorithm adjusts the path flow distribution proportion, reduces the data packet loss rate and the information age, and guarantees the stability of time delay sensitive data transmission; according to the method, resource utilization is balanced in a high-dynamic satellite network, link congestion is effectively prevented, the overall transmission efficiency and reliability are improved, and a high-adaptability and efficient transmission scheme is provided for time delay sensitive services.

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 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 smart city planning design method, and relates to the field of city planning, and the method comprises the steps: collecting monitoring data; determining two targets of land utilization rate maximization and traffic jam minimization according to the preprocessed monitoring data; an epsilon-constraint method is adopted to convert two targets of land utilization rate maximization and traffic jam minimization into a single-target optimization problem; solving a single-target optimization problem by adopting a swarm intelligence algorithm MOEAD to obtain a Pareto non-inferior solution set; performing clustering analysis on the Pareto non-inferior solution set by adopting a K-means clustering algorithm, and dividing the Pareto non-inferior solution set into K clusters according to a clustering result; and according to the preprocessed monitoring data, constructing a three-dimensional city information model by using a smart city CIM technology, mapping each solution in the representative solution set to a corresponding planning parameter in the three-dimensional city information model, and generating a corresponding city planning design scheme. The contradiction between the land utilization rate and the traffic efficiency in urban planning is balanced.

The invention relates to the technical field of vehicle network control, in particular to an efficient vehicle network control system and method based on CAN-FD, and the system comprises a data transmission optimization module, a bandwidth management module, a signal scheduling control module, an encryption security control module and an anomaly detection module. In the invention, the bit rate of a data frame, the length of a data packet and the proportion of filling bytes are optimized, the transmission efficiency is improved, the bandwidth occupied by invalid data is reduced, a priority screening mechanism is called, the stable transmission of high-priority signals is ensured, the bandwidth allocation is adjusted based on the flow state and node frequency monitoring, the burst flow congestion is relieved, and the resource allocation is balanced; in combination with arbitration priority and bus conflict state, non-emergency signal priority is optimized, key data blocking risk is reduced, encryption and integrity verification are adopted, data security is ensured, tampering and leakage are prevented, data abnormity score and detection are introduced, abnormal data are screened, identification capability is improved, and network potential safety hazard is reduced.

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 a logistics rapid distribution analysis system based on artificial intelligence, and relates to the technical field of logistics distribution analysis, in which orders, vehicle positions and traffic flow data are collected in real time, a distribution environment state is constructed, an intelligent agent is used as an independent decision unit deployed on a distribution vehicle, and the intelligent agent is used as an independent decision unit deployed on the distribution vehicle. The local planning module is used for generating a path and a resource allocation scheme, and a local scheme is shared through the communication module, so that collaborative optimization is realized, and the disadvantage that traditional static planning is difficult to deal with order changes and emergencies is solved; in the delivery process, the intelligent agent dynamically adjusts the path and task priority according to traffic and order data updated in real time, so that the loss caused by traffic congestion or order delay is remarkably reduced, and meanwhile, continuous improvement of path planning is realized through feedback adjustment; and the analysis center combines an NSGA-II algorithm to bring the distribution time, cost and task balance degree into a target function, optimizes an MADDPG strategy based on actual execution data, and improves global benefits.

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 relates to the technical field of expressway monitoring, and provides an expressway abnormal state detection method and system based on a support vector machine, which processes vehicle trajectory data and ETC gantry data through Kalman filtering and adaptive weighted average to improve the data accuracy, introduces a Gaussian kernel function to construct an SVM model, and improves the detection accuracy of the expressway abnormal state. An original feature space is mapped to a high-dimensional space to deal with a nonlinear problem, samples with labels are used to train a multi-classification SVM model, and accurate detection of various abnormal states such as traffic congestion, accidents and vehicle faults is realized through a voting mechanism. According to the method, the accuracy of abnormal state detection is improved, powerful decision support is provided for expressway managers, the traffic flow is optimized, the road traffic capacity is improved, and the development of an intelligent traffic system is promoted.

The invention, which relates to the technical field of traffic safety dispersion, discloses an anti-vehicle-blocking traffic safety dispersion method for reconstruction and expansion of an expressway, and the method comprises the following components: S1, constructing a multi-dimensional information collection system, S2, establishing an information intelligent transmission network, S3, carrying out a traffic jam prediction algorithm, and S4, carrying out precise early warning and intelligent guidance. According to the invention, rapid and efficient transmission of information is realized through the converged communication technology, the communication technology not only improves the speed and stability of data transmission, but also can realize intelligent switching in different signal environments, thereby providing timely and reliable traffic information for a traffic management center, enabling the traffic management center to make a response rapidly, and improving the traffic management efficiency. Accurate early warning and intelligent guidance are implemented, and traffic jam and safety accidents are effectively avoided.

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 an emergency facility site selection and service optimization model under facility failure and traffic jam conditions, and relates to the technical field of comprehensive facility site selection, a pre-disaster planning decision model F1 is used for site selection decision of an emergency shelter and multi-level service distribution decision with priority; the dynamic optimization model F2 under the condition of failure of the post-disaster emergency shelter is used for redistributing services between a temporary rescue point and the shelter and adjusting a route of self-evacuation crowds under the condition of user balance UE; a pre-disaster planning decision model F1 and a dynamic optimization model F2 under the condition of failure of an emergency shelter after a disaster form a double-layer mixed integer nonlinear programming model MINLP based on a priority service distribution relation, and an equivalent single-layer mixed integer nonlinear programming model is deduced according to a KKT condition. The single-layer mixed integer nonlinear programming model comprises necessary conditions and sufficient conditions for the optimal flow value of the UE road section in the dynamic optimization model F2 under the condition of failure of the post-disaster emergency shelter.

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, which relates to the technical field of intelligent traffic and illumination control, discloses a street lamp vehicle-road cooperative control system comprising a data acquisition module, a data processing module, a control execution module and a decision management module. The data acquisition module is used for acquiring traffic signal lamp phase, vehicle position and speed, street lamp working state and environment illumination intensity data; according to the invention, through a cross-system collaborative algorithm, the traffic flow direction is predicted in combination with the phase of the traffic signal lamp, the brightness of the street lamp illumination area is adjusted in advance, the energy distribution according to needs is realized, the energy consumption is effectively reduced, and through a vehicle-lamp-cloud three-level decision-making mechanism, the computing power priority is dynamically distributed according to the traffic condition. Traffic signal lamp control and vehicle driving path planning are optimized, the road passing efficiency is improved, traffic congestion is relieved, by pushing the lighting optimization path to the vehicle, the vehicle is driven under the proper lighting condition, the emergency brake risk is reduced, and the road traffic safety level is remarkably 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 provides a regional boundary traffic signal control method and system based on model predictive control, and belongs to the technical field of intelligent traffic. The method specifically comprises the following steps: partitioning a road network region; collecting traffic parameters of each region of the road network, and fitting an MFD curve of each region based on the collected data; establishing an optimization model by taking minimization of the number of vehicles in a road network as a target; an optimal boundary control value is obtained through optimization solution of an MPC controller with a built-in SLSQP algorithm, so that boundary traffic flow and boundary traffic signal lamp timing of each region of the road network are controlled; a traffic flow is simulated based on SUMO, and from a current time step to a next time step, an MPC controller with a built-in SLSQP algorithm is used for continuous iterative optimization solution to realize rolling optimization. And modeling the traffic state of each region by using MFD to obtain the flow dynamic state of the region. By establishing a network structure to simulate traffic dynamics, the MPC method is realized to control transfer of boundary traffic flow and dynamically control timing of traffic signal lamps, so that traffic congestion is relieved.

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.

A method and a device for sensing a traffic congestion, electronic equipment and a storage medium are provided. The method for sensing the traffic congestion includes: acquiring driving environment information including lane line information of a lane in which a host vehicle is located, host vehicle driving information, and vehicle information of surrounding vehicles; determining a driving path of the host vehicle based on the lane line information and the host vehicle driving information; determining an effective vehicle on at least one side of the host vehicle from the surrounding vehicles based on the host vehicle driving path and the vehicle information of the surrounding vehicles; and determining a current congestion state of a corresponding lane based on vehicle information of the effective vehicle on at least one side of the host vehicle and a historical congestion state of the corresponding lane.

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.