70 results about "Traffic speed" patented technology

The invention relates to the technical field of intelligent networked electric vehicles, in particular to an intelligent networked electric vehicle energy-saving path and economic speed collaborative optimization method, which comprises the following steps: 1) acquiring real road information of a target area based on SUMO; 2) constructing a macroscopic passing vehicle speed prediction model based on a Transform neural network; 3) predicting the passing speed of the target vehicle in each road section of the target area; 4) designing an energy consumption prediction method of the intelligent networked electric vehicle in combination with a vehicle longitudinal dynamics theory and a vehicle speed prediction result; 5) designing an efficient layered energy-saving path and economic vehicle speed collaborative optimization architecture; 6) solving an optimal energy-saving path R * by adopting an improved genetic algorithm; and 7) calculating the economic vehicle speed when the target vehicle runs on the path R * in combination with the KKT condition and a gradient descent algorithm. According to the invention, the prediction precision of the passing speed of the target vehicle in each road section can be obviously improved; the precision and the reliability of a solving result are improved; and collaborative optimization of the energy-saving path and the economic speed of the intelligent networked electric vehicle is realized.

The invention discloses a time-dependent path planning method based on a discrete space-time network, and the method comprises the following steps: discretizing a road network topological structure into a plurality of independent road sections, and constructing a first discrete space-time network; performing multi-step traffic speed prediction based on a first discrete space-time network by adopting a space-time diagram convolutional network deep learning model; integrating the prediction speed into a node connection relation between adjacent moments, and constructing a second discrete space-time network; and constructing a path planning problem based on the second discrete space-time network, converting a time-dependent path planning problem into a time-invariant path planning problem, and solving an optimal path through a polynomial time algorithm. The method can effectively capture the spatial-temporal correlation of the traffic flow, and has a polynomial time solution characteristic. According to the method, an integrated analysis framework integrating prediction and optimization is established, a time-space diagram network construction method based on data driving is provided, and the problem of real-time path optimization in a dynamic traffic environment is effectively solved.

The invention relates to the technical field of smart parking, in particular to a smart community comprehensive operation management method and device and a storage medium, and the method comprises the steps: collecting parking space data and vehicle feature data; constructing a parking space adaptation index according to the parking space data and the vehicle feature data; extracting the parking space complexity data to construct a path complexity index, constructing a parking space complexity index according to the vehicle parking space data of the parking space in the historical monitoring period, and constructing a parking space complexity factor based on the path complexity index and the parking space complexity index; performing parking space recommendation on the vehicle according to the parking space adaptation index, the parking space complexity factor and the parking space state, and optimizing the parking space recommendation process according to the vehicle passing speed; and determining a complex utilization factor according to the parking space utilization rate and the parking space complexity factor in the monitoring period, and updating the parking space recommendation process of the next monitoring period based on the complex utilization factor. The management efficiency of the intelligent community parking lot is effectively improved.

The invention discloses a traffic speed limiting method and device, electronic equipment and a storage medium, and belongs to the technical field of network security. The method comprises the steps of determining a port type of a to-be-configured port under the condition that a port configuration message sent by a control node in the virtual network is received; the port type comprises whether the port belongs to a virtual machine; under the condition that the port type is a virtual machine, determining whether the identifier of the to-be-configured port belongs to a preset white list chain or not; under the condition that the to-be-configured port belongs to the white list chain, adding a permission rule in the white list chain, and releasing the broadcast traffic of the to-be-configured port; and under the condition that the to-be-configured port does not belong to the white list chain, limiting the flow rate of the to-be-configured port according to a speed-limiting chain table rule. The problems that an existing network security technology is not completely suitable for a virtual network environment and is not high in efficiency can be solved.

The invention is applicable to the technical field of intelligent traffic systems, and provides a universal path representation learning method based on multi-modal spatial-temporal feature fusion, which comprises the following steps: firstly, carrying out multi-modal data preprocessing on a road network, a historical traffic speed and a remote sensing image, and constructing basic features; thirdly, through speed-road network fusion modeling, a road topological structure and periodic traffic speed dynamic characteristics are combined, and road path representation is generated; the road path representation and the image path representation are then subjected to fine-grained, medium-grained and coarse-grained semantic alignment at a node level, a sub-path level and a full-path level. And finally, through cross-modal residual fusion based on graph semantic information, integrating roads, images and environment semantic information thereof by using a graph neural network to obtain final path representation with enhanced semantics and stronger discrimination. According to the method, space-time dynamic and visual semantics are effectively fused, and more robust and universal path representation can be provided for downstream tasks such as path planning and traffic prediction.

The invention discloses an asphalt pavement modulus back calculation method and system based on detection data of a traffic speed deflectometer, and relates to the technical field of road engineering nondestructive testing, and the method comprises the following steps: introducing an inertia point theory into a TSD dynamic loading scene, and constructing a TSD finite element mechanical response model based on a multilayer elasticity theory and a double-wheel rolling loading characteristic; by simulating working conditions of different combinations, a direct physical relationship between deflection and the thickness of the soil matrix and the thickness of the superstructure is constructed to determine a unique soil matrix modulus, and by taking the unique soil matrix modulus as a physical boundary condition, a simulated annealing-particle swarm optimization matching method is introduced to realize dynamic modulus back calculation of the superstructure, so that the dynamic modulus of the superstructure is calculated. Therefore, an inverse calculation system based on coupling of a physical rule and intelligent optimization is constructed. According to the method, the back calculation precision and stability can be remarkably improved, the sensitivity to an initial value is reduced, the multi-working-condition adaptability is enhanced, and reliable technical support is provided for application of the TSD in road network level structure evaluation and intelligent maintenance.

The present invention provides a method, system, device, and medium for determining the construction sequence for highway maintenance, belonging to the technical field of highway maintenance. The method comprises the following steps: acquiring image data of a road section to be maintained, inputting the image data into a pavement defect detection model, and using the pavement defect detection model to detect the type, location, and severity of pavement defects; partitioning the area to be maintained according to the type and location of the pavement defects to obtain multiple sub-areas; obtaining the actual traffic area of ​​the area to be maintained before blocking; setting a traffic speed threshold for the road section to be maintained after blocking, and calculating the maximum traffic area of ​​each sub-area after blocking based on the actual traffic area; determining the maintenance sequence of the multiple sub-areas according to the maximum traffic area and the severity of the pavement defects, and maintaining the multiple sub-areas one by one according to the maintenance sequence. The present invention can improve traffic congestion during highway maintenance.

The application discloses a traffic speed prediction method based on bidirectional interaction fusion space-time graph prompt, and belongs to the field of traffic speed prediction. First, the road network data and the historical traffic speed observation data of a target region are used to construct traffic space data and a historical traffic speed sequence, and a traffic speed prediction data set is generated by using a sliding time window; a multi-scale time graph prompt is obtained by constructing a multi-scale time subgraph from the historical traffic speed sequence; and a multi-relation space graph prompt is obtained by constructing a multi-relation space subgraph from the traffic space data and the historical traffic speed sequence, the multi-relation space subgraph containing physical neighborhoods and semantic neighborhoods. The two types of prompts are subjected to feature alignment, bidirectional attention interaction and gate fusion to generate a bidirectional interaction fusion space-time graph prompt, a traffic speed prediction backbone model is input with a historical input sequence, and a future traffic speed prediction result is output. The method improves prediction accuracy and generalization ability.

The application discloses a kind of urban traffic speed estimation method based on multi-source crowd-sensing data, which is based on the road pedestrian data and on-road navigation data collected by smart phone, and the final estimated speed is obtained through the steps of missing data filling, self-view speed aggregation and multi-view speed summation.The fine-grained large-scale urban traffic speed estimation method can estimate the speed of all types of roads, including suburban sections and small roads, rather than focusing on the main roads in the city center.The application is based on data-driven, without the need to install additional devices on the road, with low cost, strong universality, stronger practicality, theoretical and applicability compared with prior art, and has important significance for improving traffic management and planning.

The invention discloses an urban road multi-lane real-time traffic state reconstruction method and device, and belongs to the technical field of intelligent traffic. Matching the vehicle license plate data with the road network topology information, and extracting path information and steering historical features; a training soft label is generated through a soft label fusion mechanism, the steering probability of an individual vehicle is predicted through an LSTM (Long Short-Term Memory) network, and macroscopic steering flow is obtained; with the matched vehicle as a reference point, estimating a lane cumulative arrival flow from the reference point to the current moment and a corresponding lane cumulative arrival curve through a DNN deep neural network; a Newell simplified motion wave model is adopted to reconstruct a lane traffic speed, the traffic speed is introduced into an IDM intelligent driving model as a constraint, a microscopic car-following track of an individual vehicle is reconstructed, and then a lane-level real-time queuing state and a traffic state are identified; the invention aims to reconstruct a continuous and fine traffic state of a whole road section from limited point detection data.

A system receives GPS data and on-board sensor data from several connected vehicles indicating locations of nearby vehicles and objects. The system processes the data to create a shared-world model that includes locations and velocities of the connected vehicles and nearby vehicles and objects, and the system determines whether driving hazards exist, such as potential collisions. The system may transmit an alert to at least one of the connected vehicles, to cause the connected vehicle or a mobile device to present a warning message to a driver, such as a visual, audio, or haptic message, or to cause the connected vehicle to implement an action to avoid the driving hazard, such as activating emergency braking or altering course. The system may create, and transmit to a connected vehicle or mobile device, a lane-level traffic model indicating traffic density, traffic speed, and traffic throughput.

The application provides a smart connected vehicle speed calculation method and system considering the fairness of the bottleneck area traffic, comprising: setting a statistical period, counting the traffic flow of the abnormal event point in the set historical period to the current time, and determining the traffic capacity of the bottleneck area formed by the abnormal event; setting control areas with different functions on the upstream of the bottleneck area according to the size of the abnormal event; calculating the average traffic speed and traffic density of the vehicles in the control area; in the flow limiting area, calculating the current traffic demand, comparing the size of the traffic demand and the traffic capacity of the bottleneck area, and adjusting the speed of the smart connected vehicle; in the speed coordination area, when the number of queued vehicles on the lane where the abnormal event occurs reaches a preset number, comparing the size of the vehicle speed on the lane where the abnormal event occurs and the vehicle speed on the adjacent lane, and reducing the speed of the smart connected vehicle on the corresponding lane. The application can control the micro driving behavior of the smart connected vehicle, guide the movement of the manually driven vehicle, and improve the traffic efficiency and safety.

The invention discloses a traffic speed monitoring method based on a Doppler frequency shift enhancement algorithm, and belongs to the technical field of traffic control, and the method specifically comprises the steps: receiving a Doppler signal flow reflected by vehicle tires in a lane in a target passing direction, and synchronously collecting the fluctuation deformation data of a surface layer material of the lane; calculating a frequency shift deviation value caused by non-vehicle motion in the Doppler signal flow according to the fluctuating deformation data, and forming a correction signal sequence after compensation; a continuous section with the frequency shift change rate being zero in the correction sequence is recognized as a stable driving track section, and the arithmetic mean value of the frequency shift amount is extracted; converting the average value into a vehicle running speed value in combination with a radar beam incident angle, and counting the distribution range of all vehicle speeds in the current signal control period; when the upper and lower limit difference value of the distribution range is continuously greater than the lane design speed proportion, triggering to generate a traffic direction green light extension instruction and sending the instruction to the signal controller; according to the invention, the accuracy of traffic speed monitoring and the adaptability of signal control are enhanced.

Method for controlling the speed of a motor vehicle, wherein the control is based on a predetermined setpoint for the vehicle speed, wherein this setpoint is adjusted depending on environmental conditions, and wherein the method comprises the following steps: Determine whether and, if so, at what distance from the current position of the motor vehicle a situation with an increased risk of reduced traffic speed is to be expected on a planned route, whereby this determination is made at least partially on the basis of traffic data available for the past; and Adjusting the target value for the vehicle speed underlying the control system depending on the result of this determination, wherein the adjustment of the target value for the vehicle speed is carried out taking into account an initial driver input, wherein this driver input defines at least one driver preference regarding the effects achieved by this adjustment; characterized in that In a case where it is determined that and at what distance from the current position of the motor vehicle a situation with an increased risk of reduced traffic speed is to be expected on the intended route, the driver is offered a selection of several different alternative target values ​​for the vehicle speed, whereby if the driver makes a second driver input by selecting one of the proposed alternative target values, the adjustment of the target value for the vehicle speed underlying the control is carried out on the basis of the selected alternative target value.

The embodiment of the application discloses a traffic speed prediction method and device based on self-attention neural control, relates to the technical field of intelligent transportation, and can improve the precision of traffic speed prediction while maintaining low calculation complexity. The application comprises: obtaining historical data of traffic speed, and generating an original input sequence; establishing an AB-NCDE model, and performing a forward propagation process on the original input sequence; establishing a loss function and performing a backward propagation process; and using the trained AB-NCDE model to predict traffic speed. The scheme is used for traffic speed prediction.

The application discloses a kind of intersection traffic speed planning method, computer equipment, readable storage medium and motor vehicle, it is related to automatic driving technical field, comprising the following steps: determining the availability of navigation map;Judge whether vehicle is close to intersection;According to signal light, judge vehicle to stop or pass intersection, and obtain the speed of road element and its rolling distance window;According to the speed of road element and its rolling distance window, the speed of passing intersection is calculated.The application does not depend on high-precision map of advanced auxiliary driving, only through navigation map, without changing the burden of road construction, effectively improve the traffic efficiency of crossroad.

The invention provides a flow rate control method and system for highway mixed traffic confluence, and relates to the technical field of traffic control, and the method comprises the steps: constructing a highway cell grid, and calculating the density information of vehicles in cells according to the lateral flow rate, the longitudinal flow rate and the ramp cell confluence flow rate from adjacent cells to a target cell; calculating the permeability of each type of vehicles in the cells and the traffic speed of the cells based on the density information of the vehicles in the target cells, and constructing a lane changing rate model according to the traffic speed and the vehicle distance information in the cells; based on the density information and a lane change rate model, constructing a lane change demand function and a cellular supply function of each type of vehicles, and updating a lateral flow rate; calculating the traffic capacity of cells based on the traffic speed, the permeability and the vehicle type information, and calculating a flow rate balance target ratio between upstream lanes of the confluence area according to the traffic capacity of the cells; and based on the flow rate balance target ratio, the lane change rate of the human-driven vehicle and the ramp cell convergence flow rate, calculating the lane change demand of the automatic driving vehicle in the upstream cell of the convergence area, and generating an automatic driving lane change strategy. According to the method, lane-level traffic is accurately modeled, and the inter-lane flow rate of the upstream of the confluence area is dynamically managed by using the active cooperative control function of the automatic driving vehicle.

The present application relates to the field of traffic speed measurement, in particular to a double radar speed measurement method for expressway section speed measurement, comprising setting a first radar speed measurement device and a second radar speed measurement device at the starting point and the ending point of the expressway section respectively, both of the radar speed measurement devices are connected with a central data processing system, and both of the first radar speed measurement device and the second radar speed measurement device have the function of simultaneously measuring the speed of multiple lanes. By setting double radar speed measurement devices at both ends of the expressway section and using the above speed measurement method, the present application can realize the whole-process accurate monitoring of the speed of vehicles, not only can accurately judge whether the average speed of vehicles in the section is overspeed, but also can accurately locate the overspeed time point and the section range of vehicles in the section, effectively improve the accuracy of expressway section speed measurement and the effectiveness of law enforcement, and provide strong technical support for guaranteeing the traffic safety of expressway.

Embodiments of the present application provide a road recognition method and related device, which can be applied to various scenarios such as artificial intelligence, intelligent transportation, assisted driving, Internet of Vehicles, etc. The method can consider traffic flow characteristics and traffic speed characteristics on the basis of remote sensing images, which can reduce the difficulty of road type recognition, and improve the recognition accuracy and recall rate. The method at least involves machine learning technology in artificial intelligence. The above method comprises: obtaining traffic flow characteristics and traffic speed characteristics of a to-be-recognized road area; obtaining a remote sensing image of the to-be-recognized road area; performing feature extraction processing on the remote sensing image to obtain road network characteristics of the to-be-recognized road area; performing road recognition processing on the traffic flow characteristics, the traffic speed characteristics and the road network characteristics based on a target road recognition model to obtain a target classification result, the target classification result being used to indicate a road type of a road in the to-be-recognized road area.

The present invention provides a deep learning-based traffic flow and charging station load forecasting method, which aims to improve the accuracy of traffic flow forecasting and accurately predict the load of electric vehicle charging stations. The method includes the following steps: First, a spatial-temporal graph convolutional network model based on the attention mechanism is used to predict future traffic flow and traffic speed. Secondly, based on the probability distribution of the vehicle's daily travel distance, a hybrid model and an expectation-maximization algorithm are used to calculate the charging demand of electric vehicles, and the energy consumption of electric vehicles is predicted using a polynomial regression model. Finally, based on the driving behavior of electric vehicles, an electric vehicle queuing model is constructed. The model comprehensively considers behaviors such as charging station capacity limitations, long waiting times, and impatience to leave, and calculates the load forecast of the charging station using a random Markov chain analysis method. This method can effectively capture the spatiotemporal characteristics of traffic flow and electric vehicle charging demand, and improve the accuracy of charging station load forecasting.

The application discloses a kind of based on quantification total risk loss Large-scale city bridge tunnel network evaluation method, the method is: one, to open source geographic platform crawler, obtain road network topology data and establish bridge tunnel network model;Two, according to road grade and intersection situation, the edge and node of bridge tunnel network model are simplified;Three, traffic speed data is converted, and daily traffic volume data is obtained;Four, the monomer structure score data in detection report is converted, and monomer structure failure probability is obtained;Five, define spatial non-homogeneous Markov chain simulation structure state evolution;Six, define the direct and indirect total loss quantification formula considering structure failure, casualties, traffic closed detour and additional carbon emission;Seven, the long-term annual total risk loss of a city large-scale bridge tunnel network is quantified and evaluated.The method solves the current evaluation bridge tunnel network method only concerns safety loss and lacks consideration of functional loss, and is suitable for efficient management and maintenance of large-scale bridge tunnel network.

The present invention discloses a method for evaluating the safety risk of traffic flow operation, comprising: obtaining the traffic density and traffic speed of the traffic flow; obtaining and obtaining a traffic safety operation index based on the vehicle distance and relative change information of different vehicles in the traffic flow; judging the traffic safety operation index, and obtaining a safety risk value based on the judgment result; fitting the relationship between the traffic density and traffic speed and the safety risk value to obtain a traffic flow risk prediction model, and identifying the traffic density and traffic speed obtained in real time through the traffic flow risk prediction model to obtain a traffic flow risk value monitored in real time.

The invention relates to the technical field of motor vehicle testing, and discloses an intelligent vehicle speed auxiliary testing method, system and device and a storage medium, and the method comprises the steps: obtaining driving video data collected on a target region road, and inputting the driving video data to a perception input module, the to-be-tested automatic driving controller carries out traffic speed limit information identification based on the driving video data and generates a corresponding control response; driving the virtual vehicle to operate in a simulation environment based on the control response, and feeding back the vehicle state information in the operation process to the automatic driving controller to form a closed-loop simulation test; and determining an intelligent vehicle speed auxiliary test result according to whether the automatic driving controller correctly recognizes the subsequent traffic speed limiting information in the continuous driving process and makes corresponding control actions.

1. Name of the product in this design: Radar Tester. 2. Application of this product: This product is used for traffic speed measurement and control. It can measure and capture the speed of vehicles in both directions, identify license plates, vehicle models and body colors, and achieve data cloud storage and synchronization through network cable or 4G. It can present data in real time and generate visual reports, and is suitable for construction areas and other scenarios. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This invention belongs to the field of intelligent transportation, specifically relating to a road network comprehensive resilience assessment method based on a dual-component neural rough differential equation. The method includes the following steps: Step 1: Constructing high- and low-frequency dual-component feature information based on historical traffic speed data and topology data of the urban road network; Step 2: Constructing a dual-component neural rough differential equation model, solving for high-frequency and low-frequency spatiotemporal feature representations, fusing the high- and low-frequency dual-component spatiotemporal features, and outputting speed prediction results; Step 3: Constructing a road network comprehensive resilience index; Step 4: Road network comprehensive resilience assessment. Experimental results show that, compared with other prediction methods, this invention not only has superior traffic speed prediction performance but also provides more comprehensive, dynamic, and reliable road network resilience assessment results, providing effective technical support for management decisions and resilience enhancement of urban transportation systems under extreme weather conditions.

The application discloses a traffic speed prediction method based on high-order principal component decomposition space-time graph convolution, constructs a spatial correlation adjacency matrix of nodes based on the distance between site nodes, and the node attribute comes from the average speed of vehicles driving at the site represented by the node; in addition, the attribute of the node has dynamic time sequence, and the whole traffic network is modeled as a space-time graph structure; the traffic space-time graph is decomposed into a core tensor and a factor matrix in each dimension based on high-order principal component decomposition, the expression form of the space-time graph convolution network after high-order principal component decomposition is inferred, and a space-time graph convolution network model of unified space-time correlation modeling is realized. The application first applies the high-order principal component decomposition algorithm to the space-time graph convolution network, starts from the perspective of high-dimensional correlation modeling in the tensor space, proposes a space-time graph convolution model based on high-order principal component truncated decomposition, and has the advantages of reducing data dimension, parallel computing and noise suppression.

1. The name of this design product: Speed ​​Limit Sign. 2. Purpose of this design product: used for traffic speed limit warning. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.

The application relates to the technical field of cloud computing networks, and provides a traffic speed-limiting system, a traffic speed-limiting method and a traffic speed-limiting device. The system comprises a network element proxy unit, a monitoring unit, a speed-limiting configuration unit, a program loading unit, a speed-limiting execution tool and a filter proxy unit. When the monitoring unit detects that the life cycle of the network element proxy unit or the bandwidth configuration strategy changes, the monitoring unit triggers the speed-limiting configuration unit to obtain a bandwidth speed-limiting execution mode and a bandwidth information configuration file according to the bandwidth configuration strategy; the program loading unit updates ip address bandwidth mapping data in a filter speed-limiting program file through the speed-limiting execution tool and the bandwidth information configuration file; the program loading unit further acquires and loads the filter speed-limiting program file into an operating system kernel, so that the speed-limiting execution tool realizes traffic speed-limiting according to ip mapping data and bandwidth mapping data in the above file and the bandwidth speed-limiting execution mode. The system can improve the scalability.

The invention discloses a multi-agent collaborative highway variable speed limit control method in a complex environment, and belongs to the technical field of highway traffic speed limit control. The method comprises the following steps: dividing an expressway into a plurality of control road sections, and obtaining traffic state data of the plurality of control road sections in the expressway in each control period and weather information and event information corresponding to the control road sections; calculating a context awareness feature vector for each control road section; constructing a multi-agent cooperative variable speed limit control model; training the Actor network and the Critic network based on the collaborative reward to obtain a trained Actor network; and during online control, calling the trained Actor network in each control period, outputting a speed limiting action according to the traffic state data and the context awareness feature vector of each control road section, converting the speed limiting action into a speed limiting control instruction, and issuing the speed limiting control instruction to a variable speed limiting sign, a road side unit or a traffic control platform for execution.

The invention relates to the technical field of traffic assistance, in particular to an unmanned aerial vehicle cooperative traffic accident handling system and a control method, and the method comprises the following steps: starting the system and entering peak daily patrol; the VSDM continuously collects and uploads the traffic flow speed and density of each road section, and the system generates a traffic flow density thermodynamic diagram according to the speed and plans a gridding fixed patrol route for the unmanned aerial vehicle; when the VSDM detects that the average speed of the road section is reduced to exceed a preset threshold value in a preset time window or the unmanned aerial vehicle returns an image to detect that the vehicle is static for a long time, triggering suspected accident early warning and recording an early warning position and associated traffic flow data; if not, returning to S2 to continue patrol; and sending a quick response instruction to the unmanned aerial vehicle closest to the early warning position. According to the invention, the problems of lagging accident discovery and confirmation, low disposal efficiency, insufficient module collaboration and low decision subjectivity are solved.