457 results about "Road congestion" patented technology

The invention provides an intelligent traffic decision-making method based on multi-source sensor data, and the method comprises the steps: obtaining vehicle sensor data and road information, and obtaining a standardized traffic flow feature vector through data synchronization, fusion, feature extraction and dimension reduction processing; inputting the standardized traffic flow feature vector into a traffic flow prediction model, and predicting the traffic flow through a vehicle dynamics model constraint state transition matrix; according to the predicted traffic flow and the real-time road condition information, the road congestion state is judged, and a real-time congestion map is generated; in combination with the real-time congestion map, the road network speed limit rule and the starting and ending point position information, an optimal path is planned, and path smoothing processing is performed through a driving behavior mode; and constructing a vehicle intelligent decision-making system, integrating traffic flow prediction, congestion detection and a path planning model, and outputting a vehicle driving strategy according to real-time vehicle sensor data.

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

A method to incorporate lane level road congestion to enhance vehicle navigation includes: collecting vehicle data from a fleet of vehicles; identifying a lane level distribution of multiple vehicles operating on a road having multiple lanes including a host vehicle; comparing the lane level distribution against a non-congested lane of the road; and identifying if a lane level congestion is occurring in at least one of the multiple lanes.

The invention discloses a smart city public safety management system based on video monitoring, and relates to the technical field of public safety management, original video data streams and a real-time road data set D are collected in real time through an API interface, a time sequence feature matrix TSM and a road congestion coefficient Ck are generated, and the data integration efficiency is improved. The behavior feature extraction module identifies group behavior features through target detection and an optical flow algorithm, and encryption transmission guarantees privacy. The risk prediction module calculates a cross-regional risk index RRI based on federated learning, triggers a response mechanism and accurately early warns risks, and the dynamic decision module combines real-time data to generate a resource scheduling scheme and selects a proper path and callable resources. Through the combination of the execution feedback module and the driving iterative optimization module, the algorithm is dynamically adjusted, closed-loop management is formed, the problems of data splitting, response lag and model stiffness of a traditional system are solved, and the real-time performance, the accuracy and the scene adaptability of public safety management are remarkably improved.

The invention relates to the technical field of traffic monitoring, and particularly discloses an intelligent traffic information processing platform, which comprises a data acquisition module, a data preprocessing module, a traffic mode recognition module, a traffic road flow control module, a traffic road flow prediction module and an information issuing module, and is characterized in that the data acquisition module acquires congested road data in real time; the data preprocessing module performs cleaning, noise reduction and formatting processing on original data acquired by the data preprocessing module; the traffic mode recognition module establishes a congestion solving model by using real-time traffic data to reduce the current road congestion situation; the traffic road flow prediction module analyzes real-time data and historical data, establishes a traffic jam prediction model, and guides a vehicle to select a route in advance. The information issuing module informs a driver of the traffic flow and the prediction result; according to the invention, the road traffic efficiency is improved, the traffic jam is effectively relieved and dealt with in advance, and the road traffic is more intelligent and efficient.

The invention provides an energy recovery control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the driving habit data and road condition data of a user, the driving habit of the user being the habit of the user controlling the vehicle speed and the rotating speed, and the road condition data being used for representing the road congestion condition; calculating a target energy recovery torque according to the driving habit data and the road condition data; and controlling the motor controller to output the target energy recovery torque. In the embodiment of the invention, the target energy recovery torque is calculated according to the driving habit and the road condition data of the user. It can be understood that the self-adaptive energy recovery mode in the embodiment of the invention is more in line with the driving habits of users, and even if different users drive vehicles, different target energy recovery torques can be calculated respectively, so that the requirements of different users for energy recovery intensity are met, and the user experience is improved.

The invention belongs to the technical field of data processing, and particularly relates to a vehicle brake reminding method and system based on big data, and the method comprises the steps: collecting environment data around a vehicle through a sensor in the vehicle, and collecting historical driving data, including driving habits of a driver, a vehicle driving track and a road congestion condition; formats of data from different sources are unified, subsequent processing and analysis are facilitated, and data from different sensors and external data sources are fused; based on the driving state of the current vehicle and the surrounding environment data, the road congestion condition and the accident occurrence probability are analyzed in real time, a machine learning or deep learning model is utilized, historical data and real-time data are combined, and the necessity and the emergency degree of braking under the current road condition are evaluated; according to the real-time analysis result and the brake risk assessment, whether the driver needs to be reminded to brake is determined, and the effects of predicting possible brake risks in the future, reminding the driver to pay attention to the front road condition and taking necessary precautionary measures are achieved.

The invention discloses a traffic signal adaptive control method based on a thunder-vision integrated vehicle detector, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining the time headway of vehicles in an entrance road section, the number of vehicles entering a target intersection, and the total number of vehicles entering the entrance road section; detecting the number of detained vehicles in each traffic flow direction of the entrance road section, and determining the saturation degree of each traffic flow direction; according to the saturation degree, the effective green light duration of the corresponding phase of each passing flow direction of the target intersection is adjusted, and when it is detected that a congestion flow direction exists, the additionally prolonged effective green light duration is added to the corresponding phase of the congestion flow direction, so that an updated signal timing scheme is obtained; and controlling the traffic lights of the target intersection according to the updated signal timing scheme in the next control period. The signal timing scheme is adjusted in real time according to the change condition of the road traffic flow state, adaptive control of the traffic signal lamp is achieved, and the road congestion problem is effectively relieved.

The invention discloses a charging station load uncertainty quantification method based on space-time traffic flow prediction, and the method comprises the steps: collecting traffic flow data from sensor nodes at two sides of a road, including traffic flow data and traffic flow average speed data; constructing a traffic flow uncertainty prediction model based on the quantile space-time network, and predicting the traffic flow uncertainty by using the model and the traffic flow data; considering the charging electricity price, traffic flow and road congestion factors of the charging station, an improved charging station queuing model is constructed, and the uncertainty prediction result of the traffic flow is converted into the uncertainty result of the load of the charging station. The method fully excavates the space-time coupling characteristics of the traffic flow data, effectively improves the accuracy of traffic flow uncertainty prediction, comprehensively considers the influence of the traffic flow, the charging electricity price and other factors on the charging station load, and further improves the precision of charging station load uncertainty prediction.

The invention provides a large road network multi-vehicle path load balancing method based on a reinforcement learning graph neural network, and the method comprises the steps: obtaining road network data in a real scene, carrying out the modeling of a road network, and representing the modeling as a graph structure; splitting the graph structure into a plurality of sub-graph structures, wherein each sub-graph structure corresponds to a sub-road network; constructing and training a reinforcement learning adaptive graph neural network; for each sub-road network of the road network, calling the trained reinforcement learning adaptive graph neural network to generate a corresponding road selection probability in parallel, combining the generated road selection probabilities of the sub-road networks according to the topological structure of the original road network, and recovering the road selection probabilities of all the roads in the original road network into complete road selection probabilities in the original road network; and realizing road load balancing based on the selection probability of each road in the complete original road network. In a large-scale road network scene, efficient path re-planning is realized, the road network load is reduced in real time, the problem of road congestion is solved, and the traffic efficiency is improved.

The invention discloses a multi-agent-based disaster prevention and risk avoiding greenbelt system layout optimization method and system, and relates to the technical field of urban planning. The method comprises the steps that original geographic data in a to-be-optimized area are received, the original geographic data are preprocessed to form effective geographic data, and the original geographic data comprise original residential area data, original road network data and original disaster prevention and risk avoiding greenbelt data; according to the invention, a multi-agent evacuation simulation model is introduced into the planning design of the disaster prevention and risk avoidance greenbelt, and multiple targets such as road congestion, greenbelt overload and the like are optimized during simulation, so that the real-time escape condition can be accurately simulated, and the real-time disaster risk of the road, the congestion risk of escape crowds and the capacity overload risk of the disaster prevention and risk avoidance greenbelt during disaster can be avoided; and an optimal escape scheme is formulated, the evacuation path and strategy can be dynamically adjusted according to different emergency requirements, and the optimal scheme under the multi-target condition is formulated.

The invention relates to an intelligent bus combined scheduling method and device and a storage medium, and relates to the field of scheduling optimization. Bus card swiping records are called to establish a card swiping station feature set, navigation station triggering is established to carry out navigation record matching analysis, and a space-time popularity prediction map is established; constructing a multi-source traffic flow data set to perform road congestion state prediction and construct a region-level road congestion state diagram; establishing a station passenger flow demand, route travel time and a congestion state according to the space-time popularity prediction map and the region-level road congestion state map; a multi-target scheduling channel is configured to take station passenger flow demands, route travel time and congestion states as input data, and route bus scheduling optimization is carried out through a passenger waiting time target, a scheduling cost target, a vehicle utilization rate target and a congestion influence target. The technical problems of insufficient data utilization, limited prediction precision and lack of flexibility of a scheduling strategy in bus scheduling are solved.

The invention discloses an intelligent logistics scheduling method based on Beidou positioning, and the method comprises the steps: obtaining the real-time position information of a vehicle, and constructing a traffic state matrix in combination with road congestion data; judging a weather influence area based on the weather change trend, and obtaining a weather risk area in combination with the traffic state matrix; obtaining inventory state data, and generating a resource demand priority list in combination with the weather risk area; adopting a multi-objective optimization algorithm, taking the resource demand priority list, the traffic state matrix and the weather risk area as input, obtaining an optimal path set, and generating a preliminary scheduling scheme; if a dynamic task insertion request is received, the task insertion feasibility is judged, the resource demand priority list is adjusted, the multi-objective optimization algorithm is operated again, and a corrected scheduling scheme is generated. Through multi-dimensional data fusion and intelligent algorithm optimization, the real-time performance, flexibility and reliability of logistics scheduling are improved, and an effective solution is provided for resource allocation in a complex environment.

The invention provides a V2X-based traffic situation early warning method, system and device, and the method comprises the steps: obtaining traffic data collected by a plurality of data sources, and carrying out the preprocessing of the received traffic data; corresponding traffic indexes are determined according to the preprocessed traffic data, and the traffic indexes at least comprise the vehicle pedestrian flow, the vehicle average speed and the lane traffic situation; wherein the lane traffic situation at least comprises a corresponding grade representing a road congestion condition in a preset time period, and the corresponding grade at least comprises one of unblocked, mild congestion, moderate congestion and serious congestion; and evaluating a corresponding traffic situation condition according to the traffic index, and if the traffic situation condition is abnormal, outputting corresponding risk early warning information. Through the real-time data monitoring technology, the urban traffic management efficiency is improved, the traffic jam problem is relieved, and the comprehensiveness and accuracy of information are ensured.

This invention discloses a method, system, and storage medium for electric vehicle charging scheduling based on dynamic data fusion, relating to the field of data processing technology. The method includes the following steps: acquiring the status monitoring parameters of all standby charging interfaces and filtering out available charging piles that meet the requirements; classifying vehicles into charging-priority vehicles and cost-priority vehicles based on the real-time status data and environmental parameters of the electric vehicles requesting charging; for charging-priority vehicles, adjusting the charging pile search range, introducing output power to determine the target charging pile and performing a locking operation; for cost-priority vehicles, calculating the cost optimization coefficient based on arrival time prediction and electricity price fluctuations within the time period, and generating a preferred charging pile sequence. The user finally selects and locks the charging pile, while dynamically adjusting the locking time according to road congestion. This method achieves efficient allocation of charging pile resources, optimizing user experience and economic costs.

The invention discloses a road congestion prediction method, relates to the technical field of urban road traffic, and solves the technical problem of low accuracy of road congestion prediction in the prior art. According to the invention, a target road is divided into a plurality of road units based on intersection positions; obtaining road basic information and historical vehicle passing information of each road unit; calculating a road congestion coefficient of the target road based on the road basic information and the historical vehicle traffic data; obtaining a road congestion coefficient prediction value of the target road in the prediction period; and comparing the predicted value of the road congestion coefficient with a preset threshold value of a road congestion range to obtain the congestion degree. According to the method, the road congestion coefficient of each road unit is calculated by performing formulation processing on the congestion point density, the traffic light period and the historical vehicle passing data, so that the influence of the road congestion coefficient on the congestion degree of the road unit can be accurately reflected in a multi-dimensional manner, and the accuracy of predicting the road congestion can be improved.

This disclosure provides systems, methods, and devices for wireless communication that support supports sidelink positioning-based traffic control. In a first aspect, a method of wireless communication includes receiving position data associated with one or more vehicles based on a positioning request. The method also includes controlling a traffic control device based on road congestion information. The road congestion information is based on the position data. Other aspects and features are also claimed and described.

The invention provides a method, a device and equipment for predicting road congestion, and a computer storage medium, relates to the technical field of traffic, and is used for improving the speed of discovering congestion of a sensing blind area road section. The method comprises the steps that vehicle driving information corresponding to an upstream area and vehicle driving information corresponding to a downstream area of a target road section are continuously obtained, the upstream area is a vehicle driving-in area relative to the target road section, and the downstream area is a vehicle driving-in area relative to the target road section; wherein the following operations are executed every time the vehicle driving information of the downstream area at a first historical moment and the vehicle driving information of the upstream area at a second historical moment meet a preset flow condition, and it is determined that the target road section is congested, when the vehicle driving information of the downstream area at the first historical moment and the vehicle driving information of the upstream area at the second historical moment meet the preset flow condition; wherein the difference value between the first historical moment and the second historical moment is not greater than a preset time range.

A method of identifying a lane-level road congestion includes modeling a lane-level congestion evolution process as a two-dimensional (2D) Markov chain having a plurality of vehicles passing through a predetermined section of a road; summing up the flow rates of the plurality of vehicles passing through the predetermined road section within the predetermined unit time; inputting data for identifying a lane congestion map of each road lane having different congestion levels; directing the lane congestion map output and the lane congestion model output to a lane route selection engine; and identifying a route and a route change of the host vehicle for application to improve a predicted time of arrival (ETA) of the host vehicle at the predetermined end position.

The embodiment of the invention provides a road congestion identification method and device, electronic equipment and a readable storage medium, and the method comprises the steps: determining a vehicle number change value based on a target number corresponding to target vehicles in a target road image and a first number corresponding to first vehicles in a first road image; determining the vehicle similarity between each target vehicle and each first vehicle based on the target vehicle feature corresponding to each target vehicle and the first vehicle feature corresponding to each first vehicle; and determining a road congestion index based on the vehicle similarity, the vehicle number change value and the congestion evaluation coefficient corresponding to each vehicle similarity. In this way, the road congestion degree is accurately evaluated based on the vehicle number change value and the vehicle similarity, and the recognition dimensions of road congestion recognition are enriched. And meanwhile, on the basis of judging whether the road is congested or not, the content of congestion identification is refined, important reference information is provided for traffic management and planning, and the traffic management efficiency is improved.

The invention belongs to the field of urban traffic management, and provides a hierarchical dynamic routing system and method for a computing power-energy fusion network, and the method comprises the steps: dividing a fusion network graph into a plurality of region sub-graphs, and dividing a path table and a connection point path table based on each region sub-graph; monitoring the running states of the road, the edge computing node and the charging station in real time, and predicting the road congestion intensity, the edge computing node queuing estimation information and the charging constraint; and determining a candidate path set in the partition path table and the connection point path table according to an online request of the vehicle, screening each candidate path in the candidate path set based on the road congestion intensity, the edge computing node queuing estimation information and the charging constraint, and determining an optimal path. According to the method, online retrieval is converted into rapid combination and evaluation of small candidate sets from full-graph traversal; traffic, energy and computing power are incorporated into the same decision framework, and systematicness deviation caused by evaluation of the future at present is avoided.

The invention belongs to the field of path planning, and discloses a mobile energy storage vehicle path planning method and system of an improved ant colony algorithm based on positive and negative excitation, and the method comprises the steps: taking the information of a regional road traffic network and a power distribution network as the input, and building a basic two-dimensional model in combination with the road trafficability affected by extreme weather and the fault state of the power distribution network; and iterating an optimal traveling path of the mobile energy storage vehicle by using an ant colony algorithm based on the established basic two-dimensional model so as to meet the power distribution demand, including forming negative feedback based on the current road trafficability so as to dynamically adjust the pheromone concentration of each path in the ant colony algorithm. According to the method, road information of a traffic department region, road accompanying conditions in extreme weather, extreme weather forecast information of a meteorological department region, power distribution network information of an electric power department region and power distribution network fault conditions in extreme weather are combined, strong correlation factors of regional road congestion in an extreme weather environment are fully considered, and the method has reasonability; and the method is more practical and is higher in applicability.

The invention relates to the technical field of traffic management, in particular to a school entrance and exit pedestrian traffic management system and method based on the Internet of Things, and the method comprises the steps: obtaining the road traffic monitoring data of a school entrance and exit in a school leaving period; the road congestion degree is evaluated by extracting vehicle distribution data; evaluating the traffic interference degree by identifying the vehicle driving direction and the mixed driving behavior of the vehicle and the pedestrian; and evaluating the school leaving speed of the students according to the road congestion degree and the traffic interference degree, and adjusting the school leaving strategy according to the school leaving speed of the students. The school leaving speed of the students is evaluated by quantifying the road congestion degree and the traffic interference degree of the school entrances and exits, the school leaving efficiency of the students is improved, and meanwhile the road traffic safety risk of the school entrances and exits is reduced.

The invention discloses a congestion relieving method and system for a highway, a vehicle and equipment. The highway congestion relieving method comprises the steps that the speed of a vehicle and a lane where the vehicle is located are obtained; judging whether the vehicle speed meets the lowest speed limit requirement of the lane or not; under the condition that the vehicle speed does not meet the minimum speed limit requirement of the lane, whether the lane is the lane with the minimum minimum speed limit or not is judged; and if the lane is not the lane with the minimum speed limit, a speed-up prompt or a slow lane change prompt is triggered. By the adoption of the method and device, when the vehicle runs on the expressway at the tortoise speed, a driver can be effectively reminded to increase the speed or change to the slow lane, so that the vehicle running at the tortoise speed is prevented from influencing normal running of other vehicles, the probability of occurrence of road congestion is reduced, and the driving safety is improved. The probability of occurrence of rear-end collision and other accidents caused by turtle-speed driving is avoided, and the driving experience on the expressway is effectively improved.

The invention relates to an intelligent traffic equipment scheduling method and system, equipment and a medium, and the method comprises the steps: obtaining road data and traffic management information of a region where intelligent traffic equipment is located, carrying out the region division processing of the road data based on the traffic management information, and obtaining the corresponding region road information; acquiring real-time equipment data of the intelligent traffic equipment, and performing association processing on the real-time equipment data and the regional road information to obtain a corresponding real-time regional traffic group; obtaining road vehicle data collected by the intelligent traffic equipment, and performing road prediction on the regional road information according to the road vehicle data to obtain a corresponding road congestion trend; performing road prediction on the emergency vehicle information based on the road congestion trend to obtain a corresponding emergency vehicle predicted passing road section; and comprehensively analyzing the initial equipment control scheme and the predicted emergency vehicle passing road section to obtain a corresponding global adjustment strategy. According to the invention, the equipment control scheme can be flexibly adjusted according to the passing demand of the emergency vehicle.

The invention relates to the technical field of traffic planning, in particular to a road congestion shunting system suitable for urban design, which comprises a node congestion identification module, a signal time sequence adjustment module, a path interference screening module, a signal priority ordering module and a linkage time period correction module. According to the method, a multi-direction traffic pressure concentration area is formed through path overlapping structure identification, an association boundary of a path group and a node state is opened, a green light starting time sequence is adjusted in combination with a column head static continuous behavior, a vehicle response and period synchronization relation is reconstructed, and structural proximity judgment is introduced in path conflict screening. High-interference paragraphs are identified by utilizing the correlation degree of guidance, speed and lane parameters, a node sorting mechanism dynamically generates priorities through the consistency of a frequent path and a queuing trend, real-time refreshing of a signal response sequence is realized, linkage rhythm correction is bidirectionally verified according to release delay and an adjacent node blocking state, a starting segment is dynamically adjusted, and the linkage rhythm is corrected. Chained dislocation is avoided.

The invention relates to a real-time reminding method and system based on vehicle driving behaviors and electronic equipment, and belongs to the field of intelligent driving. Comprising the steps of collecting monitoring data of vehicle driving in real time, inputting a pre-established violation behavior recognition model, and pre-judging violation behaviors in vehicle driving; if it is judged that the violation behavior exists or the non-standard driving action causes the safety risk, scanning a manifest file of an application program based on the vehicle-mounted voice service to verify the application capability; after verification is completed, a first reminding message is sent to the driving side so as to correct the driving behavior; and responding to the road congestion message displayed by the navigation equipment, generating a navigation plan in combination with the vehicle position and the environmental road information around the vehicle position, and sending a second reminding message to the driving side to re-plan the route. According to the scheme, the problems of lagging and unsafety caused when violation behaviors in driving cannot be judged in time or safety risks exist are solved, the optimal route can be planned again after correction, and the driving experience of a user is further improved.

The invention belongs to the technical field of logistics distribution, and particularly relates to an unmanned aerial vehicle and truck collaborative distribution method based on multi-target reinforcement learning, and the method comprises the steps: order clustering: a logistics service provider carries out the clustering of orders according to a K-means method, enables the orders with the similar distribution time and address to be divided into one order cluster, and carries out the clustering of the orders according to a K-means method; the unmanned aerial vehicles are delivered by the truck and the unmanned aerial vehicles carried by the truck; according to the NSGA-II algorithm, multiple targets such as path cost, distribution time and carbon emission are considered, and optimal paths of multiple schemes are provided; and planning an optimal path by adopting a reinforcement learning method. According to the cooperative logistics distribution method based on the unmanned aerial vehicle and the truck, the three targets of logistics distribution time, cost and carbon emission are considered, the dynamic conditions of low-altitude traffic restriction of the unmanned aerial vehicle, the road congestion condition of the truck, load constraint and the like are also considered, the optimal path is optimized by adopting a reinforcement learning mode, and the logistics distribution efficiency is improved. And parameter setting of reinforcement learning is automatically updated by using a non-dominated sorting algorithm, so that multi-dimensional optimal path selection can be provided for logistics service providers.

The invention discloses an intelligent highway abnormal event identification method based on multi-mode perception, and relates to the field of congestion identification, and the method comprises the steps: collecting the dynamic data of a highway in real time through a sensor network; feature extraction is carried out based on historical and real-time traffic flow data of each road section of the expressway, the road congestion condition is judged through feature fusion, and a congestion index is generated; based on the congestion event, triggering traceability analysis, and generating a feature vector of the source area; carrying out model training through a deep reinforcement learning model, obtaining a trained congestion reason identification and classification model, and obtaining a congestion reason label; and generating early warning information based on an identification result, and updating a navigation route through the linkage navigation platform. The method has the advantages that abnormal events on the expressway are accurately recognized by fusing video monitoring, radar, weather and toll station data, real-time traffic early warning is provided through the deep learning and prediction model, and road safety and traffic efficiency are improved.