98 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.

An energy generating speed bump assembly and a method for generating electrical energy therewith are described which integrate mechanical shock absorption with hydraulic power generation in a subterranean housing. The assembly includes a speed bump mounted within a channel configured for controlled vertical displacement. A helical compression spring connects the speed bump to a piston oil pump, converting vehicular kinetic energy into hydraulic pressure through a coordinated mechanical-hydraulic interface. The assembly includes a main oil reservoir and a compressed oil tank receiving hydraulic pressure to generate pressurized hydraulic fluid. The pressurized hydraulic fluid drives an oil turbine coupled to an electrical generator, facilitating power generation from vehicular traffic. The modular design of the assembly facilitates standardized installation within conventional roadway infrastructure. This integrated approach simultaneously provides traffic speed regulation and renewable energy generation while minimizing vehicular impact through controlled deceleration characteristics.

The invention discloses a highway traffic state estimation method based on network connection vehicle density data classification, which comprises the following steps of: 1, carrying out discretization operation on a road section and a time period so as to obtain a space-time grid; the method comprises the following steps of 1, obtaining a time-space grid, 2, obtaining network-connected vehicles in each time-space grid and vehicle track data collected by the network-connected vehicles, and calculating traffic state data, 3, extracting characteristic variables and labels of each lane in each time-space grid, and inputting the characteristic variables and the labels into a decision tree for training to obtain a density data accuracy classification model; 4, traffic speed estimation is carried out by adopting a neighborhood filling method; and 5, constructing a multi-task shared neural network traffic state estimation network and a loss function based on a traffic flow conservation model, and training to obtain traffic density predicted values of all space-time grids. According to the method, the adverse effect of the density data with large deviation on the result can be reduced, the availability of the density data collected by the network connection vehicle is improved, and the traffic state estimation on the ramp intersection road section is more accurate and more efficient.

The invention provides a method and a system for adaptive cruise based on a traffic light state. The method comprises the following steps: receiving an intersection passing speed suggested by a green wave vehicle speed guidance (GLOSA) function of a vehicle at least based on the state of a signal lamp in front of the vehicle; receiving a following speed calculated by an adaptive cruise control (ACC) function of the vehicle based on the preceding vehicle condition; and determining an adaptive cruise action to be taken by the vehicle based at least in part on a fusion of the received vehicle following speed and the intersection traffic speed. The adaptive cruise action includes adjusting a speed of the vehicle without incessant use of the ACC function.

The invention discloses a traffic speed prediction method based on a dynamic fundamental diagram and spatial-temporal characteristics, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: selecting a space region and a time range, obtaining structural data through a traffic monitoring system, and dividing a training set and a test set; a neural network model based on graph information is constructed, and the neural network model comprises a customized NF traffic flow model calibration module used for obtaining traffic flow parameters of different months and spatial positions, a spatial feature extraction module and a time feature extraction module used for extracting spatial and temporal features of traffic flow speed and flow data; and constructing a composite loss function, training the neural network model based on the graph information, and completing prediction of the traffic speed. Therefore, by adopting the traffic speed prediction method based on the dynamic basic diagram and the spatial-temporal characteristics, the result is ensured to conform to the traffic engineering theory, so that the traffic speed prediction problem under the spatial-temporal heterogeneous condition is solved, and meanwhile, the prediction precision is improved.

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 discloses an auxiliary driving method, device and system and a vehicle. The method comprises the following steps: acquiring traffic speed limit sign information around the vehicle; when the traffic speed limit sign information comprises a ramp speed limit sign, whether the vehicle drives into a ramp where the ramp speed limit sign is located or not is determined; and controlling the speed of the vehicle according to the judgment result. According to the method, the device, the system and the vehicle, on the basis of determining that traffic speed limit sign information identified by a current vehicle comprises a ramp speed limit sign and determining whether the current vehicle is driven into a ramp where the ramp speed limit sign is located, the speed of the vehicle is controlled according to a judgment result. The problem that the traffic speed limit sign is mistakenly recognized when the vehicle runs on the main road can be effectively avoided, and the experience feeling of a driver and the driving safety are improved.

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 present application relates to a method and apparatus for limiting the speed of a cluster's shared traffic threshold, which is used to achieve effective bandwidth management and allocation between multiple nodes in a cluster and between multiple network card queues within a node. By using a token bucket algorithm, traffic speed limiting resources are dynamically allocated in a distributed environment to ensure that each node and queue can operate within a reasonable speed limit, so as to achieve effective traffic management between each node, queue, and CPU core in the cluster. The present application can balance bandwidth utilization between different nodes and different network card queues on the same node, improve network performance and resource utilization efficiency, and achieve effective bandwidth speed limiting for a cluster.

The invention discloses an expressway passing method and device in rainfall weather, equipment and a storage medium, and relates to the field of electric digital data processing, the method divides an expressway into a plurality of cells for separate management, the rainfall condition of each cell is known in advance through the rainfall prediction data, and the rainfall condition of each cell is determined according to the rainfall prediction data. The traffic speed of each cell is limited through the weight coefficient, so that the larger the precipitation is, the lower the traffic speed is, and finally, the corresponding traffic parameters are obtained by detecting the cell where the vehicle is located through the radar, so that the one-step closed management of the highway is effectively prevented, and the travel convenience is ensured.

A method of managing traffic lights that generates waves of unimpeded traffic in both directions of a roadway. The roadway is divided into an even number of zones of equal length, which are divided into two groups of alternating zones. A traffic speed and a traffic light duration are selected so that the zone length equals the traffic speed times the light duration. Initially all lights in all zones of one group are set to green, and all lights in all zones of the other group are set to red; then lights are switched in the next cycle to red for the first group and green for the other group. This pattern repeats. The arrangement of alternating zones and the relationship between zone length, speed, and light duration allows vehicles moving in either direction at the specified speed to encounter only green lights, enabling dramatic reductions in travel time.

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 utility model discloses a traffic speed limiting device of a traffic signboard, and relates to the technical field of traffic speed limiting. The device comprises a speed limiting device body, the outer surface of the speed limiting device body is provided with a plurality of sets of quick replacement mechanisms, and each quick replacement mechanism comprises a label, a push block, a pull rod and a spring; the label is arranged on the front face of the speed limiting device body, the push block is slidably connected to the outer surface of the speed limiting device body, one side of the push block abuts against the label, the pull rod is fixedly installed on the other side of the push block, and the spring is wound around the outer surface of the pull rod. According to the device, the sign and the speed limiting device are separated, so that the sign is aged, only the sign needs to be replaced, the whole sign does not need to be replaced, it is guaranteed that the sign cannot be eroded by water through the sealing mechanism, the service life of the sign is prolonged, the sign is fixed through a single bolt, the time needed for replacement is greatly shortened, efficiency is improved, and the requirements of a user are met.

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 invention relates to the technical field of expressway speed limit mark shielding, and discloses a traffic sign shielding device which comprises a walking mechanism, a lifting clamping jaw assembly and a hanging frame assembly. A plurality of left floating wheel sets and a plurality of right floating wheel sets are arranged on a left side frame and a right side frame of the walking frame respectively, the walking driving mechanism drives the left floating wheel sets and the right floating wheel sets to walk, each of the left floating wheel sets and the right floating wheel sets comprises a wheel frame, a roller and an arc-shaped anti-collision block, the rollers are installed on the wheel frames, and the arc-shaped anti-collision blocks are installed on the roller frames. The walking driving mechanism drives the rolling wheels to walk along the steel pipe track through the transmission assembly, and the arc-shaped anti-collision blocks are installed on the inner sides of the wheel carriers. According to the traffic sign shielding device provided by the invention, the problem of low efficiency of manually shielding the traffic speed sign board is solved.

The present invention relates to the field of distribution route optimization, and discloses a method for optimizing the route of green cold-chain vehicles considering time-varying speed, which includes the following steps: obtaining the continuous dynamic change information of the vehicle driving speed, simulating the real road network traffic speed function through a step function, and deriving the linear piecewise travel function between any two customer nodes; establishing an optimization model for the route of green cold-chain vehicles with the goal of minimizing the sum of vehicle transportation costs, vehicle refrigeration costs, vehicle carbon emission costs, and labor costs; designing a two-stage hybrid search algorithm to solve the optimization model for the route of green cold-chain vehicles, and simultaneously optimizing the vehicle route and the departure time of each customer node.

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 invention discloses a large-scale urban bridge and tunnel network evaluation method based on quantitative total risk loss, and the method comprises the steps: 1, carrying out the crawling of an open-source geographic platform, obtaining the topological data of a road network, and building a bridge and tunnel network model; 2, simplifying edges and nodes of the bridge and tunnel network model according to road grades and intersection conditions; 3, converting the traffic speed data to obtain daily average traffic volume data; 4, performing conversion on the single structure score data in the detection report to obtain a single structure failure probability; 5, defining spatial inhomogeneous Markov chain simulation structure state evolution; 6, defining a direct and indirect total loss quantification formula considering structural failure, casualties, traffic closed detour and additional carbon emission; and 7, carrying out quantitative evaluation on the long-term annual total risk loss of the large-scale bridge and tunnel network of a certain city. The method overcomes the defect that the current bridge and tunnel network evaluation method only concerns the safety loss and lacks consideration on the function loss, and is suitable for efficient management and maintenance of the large-scale bridge and tunnel network.

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.