49results about How to "Ease traffic congestion" patented technology

The invention discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

The invention relates to a lockage ship reservation scheduling optimization method. The method comprises the following steps: S1, obtaining ship information of a water transportation junction, attribute information of a ship lock and ship remote lockage declaration information; s2, taking the minimization of the ship lock waiting time and the ship reservation adjustment rate as optimization objectives, constructing a ship reservation scheduling double-objective optimization model, and establishing constraint conditions satisfied by lockage ship reservation scheduling; and S3, performing iterative solution on the ship reservation scheduling dual-objective optimization model based on a fast non-dominated sorting genetic algorithm to obtain an optimized lockage ship scheduling scheme. The technical problems that existing ship lock resources are not enough in utilization and the ship dam passing efficiency is not high are solved, the waterway traffic jam problem can be effectively relieved, the overall ship lock passing efficiency can be improved, the ship waiting time is shortened by optimizing the matching relation between the ship dam passing number and the ship lock service capacity, and the ship dam passing efficiency is improved. And the ship reservation adjustment rate is reduced, so that the ship lockage efficiency is improved.

The invention discloses a processing method for dynamically adjusting a traffic light signal scheme according to traffic flow, which comprises the following steps of: deploying sensors including a ground induction coil, a camera and a pedestrian button at each traffic intersection, and preprocessing collected traffic flow and pedestrian flow data; and predicting trends of traffic flow, non-motor vehicle flow and pedestrian flow of each intersection in a short period, modeling traffic light states of each intersection into a dynamic system, calculating an optimal traffic light adjustment strategy, and adjusting the traffic light states in real time. By dynamically adjusting the traffic light signals, the system can more effectively respond to the actual on-site conditions including the flow of pedestrians and non-motor vehicles, more reasonable signal duration distribution is achieved, waiting time is shortened, traffic efficiency is improved, meanwhile, the pedestrians and the non-motor vehicles are considered, the safety of the system can be improved through an optimized signal control strategy, and the traffic light control method is suitable for popularization and application. The signal lamp is adjusted to provide more transition time for pedestrians and non-motor vehicles, so that the accident risk is reduced.

The invention discloses a traffic transportation emergency command and dispatch system based on big data resources, which is constructed based on the big data resources and mainly comprises a data acquisition module, a big data processing and analysis module, an event early warning and prediction module, an emergency command and dispatch module, an information display and interaction module and the like. Various devices (a sensor, a camera, a GPS and the like) are utilized to comprehensively collect data such as traffic flow, road conditions, weather, events and the like. Acquisition has the characteristics of high frequency and high precision, supports a plurality of data transmission modes and protocols, and can integrate and format the acquired data to guarantee data quality and convenience of subsequent processing. According to the system, effective utilization of traffic data is realized through collaborative operation of all the modules, and the traffic transportation emergency management capability is improved from multiple aspects, including improvement of management efficiency, enhancement of emergency response capability, improvement of decision support, enhancement of information transparency, optimization of resource allocation, reduction of economic loss and improvement of public safety.

The invention discloses a variable curvature complex road condition mixed traffic flow-oriented CAVs car-following queue forming method. The method comprises the steps of obtaining state information of confluence CAVs and a confluence center of an initial CAVs queue; performing confluence scene division according to the state information of the confluence CAVs; for the collaborative lane-changing confluence scene, establishing a collaborative lane-changing game model considering the compromise degree of a human driver based on the confluence CAVs, the confluence center and the position state of HVs in the confluence direction, and generating a collaborative lane-changing confluence decision; for an overtaking lane-merging confluence scene, generating an initial confluence track based on road surface information by using a dynamic programming algorithm; constraining the initial confluence trajectory through a double-layer quadratic programming algorithm to generate an overtaking lane-merging confluence decision; and the confluence CAVs complete confluence through a collaborative lane changing confluence decision or an overtaking lane changing confluence decision, and join the confluence into the initial CAVs queue to form a CAVs car following queue. According to the method, the influence of HVs and road conditions (environmental information) on the formation of the CAVs car-following queue in a mixed traffic scene is considered.

The invention belongs to the technical field of intelligent traffic, and particularly relates to an expressway traffic state judgment method based on vehicle-level macro and micro evaluation. The method comprises the following steps: firstly, acquiring millimeter-wave radar data of a target road section in a target time period, and preprocessing the millimeter-wave radar data; then, according to millimeter wave radar data, evaluation indexes are calculated, and the evaluation indexes comprise a microcosmic index and a macroscopic index; the correlation between the indexes is checked, part of indexes with high correlation are removed, and the obtained microcosmic indexes comprise the vehicle drift angle variance, the lane changing behavior frequency, the time headway variance, the acceleration variance and the aggressive driving behavior variation index variance; the macroscopic indexes comprise average flow, a speed variation coefficient, a cart mixing rate and a congestion index; and finally, based on the macro and micro evaluation indexes, calculating the traffic state entropy of each road section in the target road section in the target time period, and classifying the traffic state according to the traffic state entropy. According to the method, the aggressive driving behavior variation index is provided for measuring the vehicle danger degree caused by sudden acceleration, deceleration or too high driving speed, so that the traffic state is reflected more finely from the microcosmic level, and meanwhile, the traffic state is accurately quantified by considering the mutual influence among the road sections.

The invention relates to the technical field of urban governance, in particular to a method for optimizing an urban commuting pattern by using an artificial intelligence algorithm, which comprises the following steps of: identifying urban employment center data according to mobile phone signaling data, and summarizing and preprocessing the data; utilizing a Gaussian mixture model to identify space relative distribution characteristics and influence weights of the employment places and the employment centers; identifying a space relative change rule of a residence place and an employment place through an XGBoost model, and analyzing a job-residence relationship; solving the gravity model by using an XGBoost model to identify a commuting rule between the residence place and the employment place so as to obtain a commuting structure; simulating employment center adjustment through an intelligent optimization algorithm, and predicting changes of an employment place, a residence place and a commuting flow to generate a commuting pattern; the commuting pattern is evaluated by collecting actual commuting data and user feedback. The method can provide data support and scientific guidance for urban public facility pattern, urban quality improvement and urban fine governance.

The invention relates to a system and method for generating a base path for autonomous vehicle motion control. In particular, a computing system can obtain a target nominal path. The computing system can determine a current attitude for the autonomous vehicle. The computing system can determine a lane change zone based at least in part on a current attitude of the autonomous vehicle and the target nominal path. The computing system can determine one or more merge points on the target nominal path. The computing system can generate, for each respective merge point of the one or more merge points, a candidate base path from a current attitude of the autonomous vehicle to the respective merge point. The computing system can generate a suitability classification for each candidate base path. The computing system can select one or more candidate base paths based on the suitability classification for each respective candidate base path of the plurality of candidate base paths.

The invention provides a vehicle-mounted detecting and guiding system during advancing, and belongs to the field of vehicle control. The problem that the vehicle auxiliary driving efficiency is low is solved. Specifically, the data acquisition module acquires driving data; the positioning correction module is used for carrying out primary analysis on the driving data and correcting the position of the vehicle; the driving assistance module carries out primary adjustment on the vehicle speed; analyzing the power oil consumption change of the vehicle according to the driving data, obtaining the remaining driving time of the vehicle to the destination, and carrying out secondary adjustment on the driving speed of the vehicle; the continuous adjustment module continuously obtains driving data and destination information of the vehicle and adjusts the positioning and driving speed of the vehicle; according to the invention, the positioning and speed of the vehicle are adjusted by acquiring, analyzing and processing related data of vehicle driving, so that the vehicle is stable in driving.

The embodiment of the invention provides a vehicle lane changing control method, device and equipment, an automobile and a storage medium, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring a first space income and a second space income of a vehicle within a preset time period; the first space revenue is the revenue obtained by simulating that the vehicle runs according to a target lane changing planning track, and the target lane changing planning track is a track which is determined based on a preset dynamic planning algorithm and consumes the minimum total cost in lane changing longitudinal planning tracks at which the vehicle can change to a target position of a target lane; the second space revenue is the revenue obtained by simulating the self-vehicle to run along the target cruise planning track; determining lane changing income according to the first space income and the second space income; and controlling the own vehicle to change the lane according to the lane changing income. The method is used for achieving the effects of improving the accuracy of the lane changing decision and improving the passing efficiency of the vehicle in complex scenes such as medium and low speeds.

The application discloses a charging control method and system of an electric vehicle, applied to the technical field of charging control, and comprises the following steps: obtaining historical charging data of each electric vehicle to be charged; extracting charging behavior features from each historical charging data, and determining the loss degree of each electric vehicle to be charged based on the charging behavior features; obtaining a corresponding power consumption influence coefficient according to the loss degree of each electric vehicle to be charged; generating an initial charging scheme based on the geographical distribution data of charging facilities and the real-time position information, trip information and power consumption influence coefficient of each electric vehicle to be charged; optimizing the initial charging scheme to obtain a charging control scheme of all electric vehicles to be charged; and sending a control instruction to each electric vehicle to be charged, wherein the control instruction is obtained from the charging control scheme. The charging control method and system of the electric vehicle provided by the application effectively improve the reliability of charging control.

The application discloses a multi-terminal positioning intelligent cooperative management system based on Beidou satellites and relates to the technical field of operation and maintenance data intelligent management. The application collects crossroad vehicle information, sets a collection period and a monitoring range, counts vehicle information, calls historical data, extracts historical reference vehicle data, calculates historical traffic demand, analyzes the influence degree of traffic prediction, obtains traffic demand of the next monitoring period, monitors congestion crossroads, analyzes green light adjustment demand, adjusts the green light duration, monitors the congestion of adjacent crossroads after adjusting the congestion crossroads, adjusts the green light duration of the adjacent crossroads, and is used for synchronously and reversely adjusting the red light duration while adjusting the green light duration. The application accurately analyzes the traffic demand of each direction, monitors the traffic conditions of adjacent crossroads, and is adaptively adjusted to avoid the transfer of traffic pressure, so that the traffic operation of the whole region is more smooth.

The invention discloses a random access network information timeliness optimization method based on information correlation. The method specifically comprises the following steps: 1) constructing a wireless communication network model comprising a plurality of information sources and base stations; 2) defining a correlation matrix between the information sources, and describing an information sharing probability between the information sources; 3) deriving an expression of network average AoI; and 4) using an optimization algorithm to jointly consider the information correlation and the random access probability, and minimizing the average AoI of the network. Simulation results show that compared with a traditional random access strategy ignoring information correlation, the method of the invention significantly reduces the average AoI of the network, and improves the timeliness and stability of information transmission. The method is suitable for time-delay-sensitive wireless communication scenes such as the Internet of Things and video monitoring.

The application provides a low-carbon travel carbon emission reduction amount verification method and system based on travel chain big data, which comprises the following steps: step 1) obtaining sample data of travel data; step 2) when the sample data is walking data, going to step 3); when the sample data is bicycle data, going to step 4); when the sample data is electric bicycle data, going to step 5); step 3) verifying the sample data by using a walking travel distance verification method and removing duplicates to obtain a final effective travel distance; calculating the carbon emission of walking according to the final effective travel distance; step 4) verifying the sample data by using a bicycle travel distance verification method and removing duplicates to obtain a final effective travel distance; calculating the carbon emission of the bicycle according to the final effective travel distance; step 5) verifying the sample data by using an electric bicycle travel distance verification method and removing duplicates to obtain a final effective travel distance; and calculating the carbon emission of the electric bicycle.

The embodiment of the invention provides an intelligent traffic-oriented spatio-temporal data feature mining method and system, and the method comprises the steps: generating a traffic spatio-temporal knowledge network through the real-time obtaining and processing of the dynamic traffic flow data of a plurality of traffic participants, and marking and analyzing a first spatio-temporal vertex and a target dominant participant in the traffic spatio-temporal knowledge network. According to the invention, the method can accurately recognize the key participant which has the greatest influence on the traffic flow, carries out the deep feature mining of the main body moving state track data of the key participant, can find potential traffic accident risk points in advance, remarkably improves the early warning capability of an intelligent traffic system, and facilitates the reduction of traffic accidents. Based on the dynamic traffic flow data and the analysis result of the key participants, traffic resources are allocated more scientifically and reasonably, a traffic signal control strategy is optimized, the road passing efficiency is improved, traffic congestion is reduced, and the efficiency and effect of urban traffic management are enhanced.

The invention discloses a cross intersection induction type all-red time calculation method. The method comprises the steps that S1, intersection linear parameters are acquired through a video detector; s2, speed information of pedestrians, non-motor vehicles and motor vehicles crossing the street is obtained through a video detector, and a speed threshold value is calculated according to the distribution frequency; s3, calculating the full-red basic duration of clearing the pedestrians, the non-motor vehicles and the motor vehicles crossing the street; s4, the crowding condition is judged, and the all-red time of pedestrians, non-motor vehicles and motor vehicles crossing the street is calculated; and S5, obtaining the final all-red time. According to the cross intersection induction type all-red time calculation method, the congestion conditions of pedestrians, non-motor vehicles and motor vehicles crossing the street are detected in real time, and the dynamic change of the traffic flow is taken into influence factors for calculation, so that the change of the induction type all-red time is realized, the accuracy of the calculated all-red time is improved, the control delay of the intersection can be reduced, and the control accuracy of the intersection is improved. The traffic flow is optimized; and the traffic jam is reduced.

The invention discloses an expressway collaborative management and control method, device and equipment and a storage medium. Comprising the steps that constraint conditions and a tool set issued by an operation side are acquired, the constraint conditions are used for constraining behavior boundaries of management and control measures of the operation side, and the tool set comprises management and control measures of economic dimensions; obtaining a constraint condition and a management and control measure set of the operation side, fusing the constraint condition of the operation side with the constraint condition of the operation side to obtain a collaborative constraint condition, and fusing the tool set of the operation side with the management and control measure set of the operation side to obtain a collaborative management and control measure set; in response to a to-be-handled event occurring on the highway, performing optimization solution based on event related information, a collaborative constraint condition and a collaborative management and control measure set, and generating a joint optimization decision; and executing a joint optimization decision, and handling the event. According to the invention, real-time cooperation of operation management and control and operation targets is realized, and the overall consistency and comprehensive benefits of disposal decisions are improved.

The invention belongs to the field of curb parking in intelligent parking, and relates to an order-based curb parking reservation management system, which comprises an information acquisition module, a parking reservation module and an information feedback module, firstly, a parking order degree model is designed and feasibility of the model is verified; secondly, setting the parking order degree model in a parking reservation module; then, after the information of the on-road parking system is obtained through the information obtaining module, the optimal parking space is judged through the parking reservation module, and finally, the leaving vehicle is fed back through the information feedback module. According to the technical scheme adopted by the invention, the on-road cruise time for finding the parking space by the vehicle can be shortened, the utilization rate of the parking space is improved, and traffic congestion and road traffic accidents caused by finding the parking space can be reduced to a certain extent.

The invention, which belongs to the technical field of automatic driving monitoring, discloses an automatic driving-based monitoring system for a self-avoidance subway construction road section, comprising a vehicle automatic driving system, a subway construction monitoring center and a high-altitude monitoring system. The vehicle automatic driving system, the subway construction monitoring center and the high-altitude monitoring system are all connected with a local emergency center and a traffic management department through a cloud end, the high-altitude monitoring device is further connected with an auxiliary remote demonstration connector, and large-area real-time monitoring is conducted on a subway construction road section through the high-altitude monitoring device. Road condition information of a subway construction road section is collected in real time, and the road condition information is sent to vehicles in a current preset area range and a vehicle automatic driving system in an automatic driving vehicle through a cloud end, so that traffic jam around a subway construction area is reduced, the traffic capacity of the surrounding area is improved, and traffic safety is guaranteed. And the occurrence probability of traffic accidents is reduced.

The application relates to the technical field of traffic signal control, and particularly discloses an adaptive traffic signal control method based on reinforcement learning and a self-attention mechanism, which comprises the following steps: establishing a Markov decision process model and a policy neural network based on a deep reinforcement learning algorithm; building a deep Q network and a self-attention mechanism based on the Markov decision process model; evaluating the policy and updating the neural network of the policy neural network, an encoder and a decoder; performing multiple iterative training on the neural network; obtaining a trained neural network model; and generating real-time traffic signal control signals according to the trained neural network model. The method can gradually learn an optimal policy in a complex traffic flow. In addition, the algorithm adopts an encoder-decoder structure, stores historical trajectories in a replay buffer, and uses proximal policy optimization to update parameters. The method solves the problem that the existing adaptive traffic signal control method needs a large amount of data for training and requires a large amount of computing resources.

The application discloses a highway cooperative decision safety driving method, comprising the following steps: S1, acquiring vehicle end sensing information and road end sensing information and performing information fusion; S2, establishing a plane rectangular coordinate system, and establishing a speed potential field, a lane potential field, a road boundary potential field and a solid obstacle potential field on a road surface according to the fused sensing information; wherein the speed potential field enables a vehicle to travel at a set expected speed, the lane potential field enables the vehicle to travel in a current lane, the road boundary potential field enables the vehicle to travel away from a road boundary, and the solid obstacle potential field enables the vehicle to travel away from a solid obstacle; S3, a traveling vehicle is affected by a total potential field formed by superposition of the speed potential field, the lane potential field, the road boundary potential field and the solid obstacle potential field, and target accelerations of the vehicle in a driving direction and a transverse direction are obtained according to a potential field intensity gradient of the total potential field at a vehicle position in the driving direction and the transverse direction; and S4, the vehicle travels according to the target accelerations in the driving direction and the transverse direction.

The present invention discloses a method for optimizing a logistics distribution network, comprising the following steps: S1, determining a set of candidate points of a distribution center in a city; S2, constructing a logistics distribution network optimization model according to the distribution center; S3, iteratively optimizing the logistics distribution network optimization model using an improved genetic algorithm; S4, using the iteratively optimized logistics distribution network optimization model to layout the logistics distribution network, completing the optimization of the logistics distribution network, and providing an effective reference for the optimization of the logistics distribution network of a third-party logistics enterprise.

The invention discloses a traffic signal control method and system based on a traffic signal controller. The method comprises the following steps: collecting multi-dimensional traffic data of a target road; calculating a traffic stability index used for representing the traffic stability degree of the target road according to the multi-dimensional traffic data; based on the multi-dimensional traffic data and the corresponding traffic stability index, determining the passing duration of a traffic signal lamp corresponding to the target road; and sending the passing duration to a traffic signal controller used for controlling the traffic signal of the target road so as to control the traffic signal lamp. By utilizing the embodiment of the invention, the traffic condition of the target road can be more comprehensively reflected, and the passing time of the signal lamp is dynamically adjusted according to the change of the traffic flow, so that the efficiency and the flexibility of signal control are improved, and positive contributions can be made for relieving urban traffic congestion, reducing emission and improving public travel safety and comfort.

The invention discloses a car-sharing car-hailing method and device, electronic equipment, a readable storage medium and a chip, and relates to the technical field of hitchhiking, and the car-sharing car-hailing method comprises the steps: obtaining at least one piece of historical car-hailing information for completing car sharing of a first passenger user, each piece of historical car-hailing information comprises a first passenger user and at least one second passenger user; determining a second passenger user corresponding to the historical car-hailing information in response to a car-sharing invitation instruction of the first passenger user for the historical car-hailing information; determining first car-sharing information of the first passenger user and / or second car-sharing information of the second passenger user; determining order information according to at least one of the first car-sharing information and the second car-sharing information and at least part of the historical car-hailing information; and generating a car-hailing order according to the order information.

The invention discloses a driving safety detection method, and the method comprises the steps: obtaining auxiliary data which comprises at least one of battery system data, driving system data, driving posture data and environment detection data; based on the auxiliary data, determining fusion data; acquiring historical operation data; and determining the vehicle driving risk based on the historical operation data and the fusion data. According to the method, the safety of the pure electric commercial vehicle in the driving process is ensured through multi-dimensional detection and intelligent analysis.

This invention relates to the field of vehicle management, specifically to a data management system and method based on vehicle traffic planning. The system includes an image perception module, a data fusion module, a path guidance module, a vehicle tracking module, and a replanning module. The image perception module identifies vehicle boundaries, the data fusion module performs indoor vehicle positioning, the path guidance module plans pedestrian and vehicle routes, the vehicle tracking module establishes a kinematic model and labels matched vehicles, and the replanning module recalculates the merging path to minimize the estimated merging time or travel cost. This invention can supplement missing image details, making the identification of key features of vehicles and pedestrians clearer, improving target detection efficiency, reducing waiting time for pedestrians and vehicles, alleviating traffic congestion in complex road conditions, shortening vehicle pick-up time, improving road capacity, and achieving two-way dynamic matching and visualized path collaboration between pedestrians and vehicles.

The application provides a vehicle scheduling method and device, a medium and a quantum computer. The method comprises the following steps: obtaining scheduling reference data; determining a plurality of candidate scheduling schemes according to the scheduling reference data, and determining, in each candidate scheduling scheme, a first time required for each to-be-parked vehicle to arrive at a corresponding parking space and a second time required for each to-be-parked vehicle to park in the corresponding parking space; determining a total cost of each candidate scheduling scheme according to the first time and the second time of each to-be-parked vehicle in the candidate scheduling scheme; determining a target scheduling scheme from the plurality of candidate scheduling schemes according to the total cost of each candidate scheduling scheme; and guiding the plurality of to-be-parked vehicles to park in the corresponding parking spaces based on the target scheduling scheme. By introducing the parking time of the vehicle into the selection of the path scheme, the traffic congestion caused by the parking operation is reduced, the traffic flow in the parking lot is ensured to be smooth, and the space utilization and the turnover rate of the parking lot are improved.

This invention relates to the field of Industrial Internet of Things (IIoT) technology and discloses a control method for collaborative adaptive routing between industrial edge cloud and digital network in low-network, high-concurrency environments. The method includes: comparing geospatial coordinates with geofences; determining whether to trigger a context engine based on the comparison results or changes in the status of digital work orders; if the context engine is triggered, clustering the work order semantics corresponding to the changed digital work order status in the cloud vector space based on the geospatial coordinates and / or the work order semantics, identifying and synchronizing a subset of hot knowledge points; performing emergency quantization, bandwidth probe evaluation, and complexity evaluation in parallel on industrial query requests to determine the maximum tolerable latency, uplink and downlink bandwidth, and total cloud latency; if the edge device is offline, encapsulating the data generated during the offline state into an offline cognitive log and performing local retrieval based on a knowledge base. This invention balances low-latency response in low-network environments with routing stability for high-concurrency requests.

The application discloses a kind of holographic traffic perception method and system based on wide-area radar, belong to vehicle traffic control technical field, for solving the technical problems of existing traffic perception system, it is difficult to provide real-time, holographic traffic information service for mass vehicle, not conducive to optimizing traffic flow space-time distribution, and relieving congestion and reducing accident rate.Method includes: the coordinate data uniform processing of initial tracking data and initial driving data about latitude and longitude offset, respectively corresponding to obtain radar tracking data and target driving data;Radar tracking data and target driving data are calculated about the trajectory topology of target reference system, respectively to obtain first state estimation information and second state estimation information;And generate minimum topology distance optimization model;Based on minimum topology distance optimization model, relative position of multi-vehicle target is distinguished and identified, to obtain vehicle holographic information;Realize holographic traffic perception to global vehicle target.

The present invention relates to the field of traffic management technology, and in particular to a method and system for emergency control of highway toll stations based on deep learning. The method includes obtaining vehicle data information, including obtaining historical vehicle data and real-time vehicle data of a highway section, preprocessing the obtained vehicle data, including cleaning and smoothing the obtained vehicle data, and normalizing the processed vehicle data; constructing a prediction model based on the preprocessed vehicle data, training the model according to the constructed prediction model, inputting the obtained real-time vehicle data into the trained prediction model, and obtaining the prediction result of the traffic volume in the future time; generating an emergency control strategy according to the prediction result, and the present invention can obtain vehicle data in real time and input the trained prediction model in time, so as to alleviate traffic congestion to the greatest extent and improve traffic efficiency.