135 results about "Traffic speed" patented technology

The invention discloses a wireless control method for LED illumination, a dimming device, a medium and a product, and relates to the technical field of electric illumination, and the method comprises the steps: obtaining tunnel illumination control parameters; dividing the tunnel entrance area into an entrance section, a buffer section and a basic section according to the tunnel parameters, and determining a plurality of illumination control subareas; calculating the brightness adaptation time of a vehicle driver based on the external brightness value and the vehicle passing speed, and generating an illumination gradient curve from the entrance section to the buffer section and the basic section; according to the illumination gradient curve, determining a target illumination value of each illumination control zone; converting the target illumination value into an LED driving parameter, and issuing the LED driving parameter to an LED driving circuit of each illumination control subarea; and collecting actual illumination data of each illumination control subarea, and regenerating an illumination gradient curve when the actual illumination data exceeds a preset target range or the external brightness value changes. By implementing the method, tunnel illumination can be adjusted based on illumination changes when the weather changes drastically, and driving safety is guaranteed.

The invention provides a traffic state data interpolation method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining original GPS data, carrying out the preprocessing of the original GPS data to obtain a preprocessed vehicle track data set, and generating a traffic speed tensor based on the preprocessed vehicle track data set; based on the traffic speed tensor, constructing a heterogeneous graph attention network model considering space and time factors in space and time perspectives; utilizing the heterogeneous graph attention network model to extract potential features of a space view angle and a time view angle, and performing feature fusion on the potential features to perform interpolation of missing traffic state data; and outputting an interpolation result of the traffic state data so as to realize accurate estimation of missing data. According to the invention, through fusion of space and time heterogeneous features, robust interpolation can be provided for the traffic state under different missing proportions, so that the traffic state can be predicted more accurately.

Techniques for dividing a lane into a first virtual lane and a second virtual lane are described. A narrow track vehicle may determine that the narrow track vehicle is traversing a lane that is at least 10 feet wide. The narrow track vehicle may divide the lane into a first virtual lane and a second virtual lane and traverse one of the first virtual lane or the second virtual lane based at least in part on location (e.g., when traversing a highway lane, residential lane, etc.) and / or speed of traffic (e.g., when traffic is traveling at or below a threshold speed). In examples, the narrow track vehicle may traverse the environment in between a first lane and a second lane upon determining that a distance between a first object in the first lane and a second object in the second lane meets a threshold distance.

The invention 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 agent unit, a monitoring unit, a speed limiting configuration unit, a program loading unit, a speed limiting execution tool and a filter agent unit. When the monitoring unit monitors that the life cycle or the bandwidth configuration strategy of the network element agent unit is changed, the speed-limiting configuration unit is triggered 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 the filter speed limiting program file through the speed limiting execution tool and the bandwidth information configuration file; and the program loading unit also acquires and loads a filter speed limiting program file to an operating system kernel, so that a speed limiting execution tool realizes flow speed limiting according to ip mapping data and bandwidth mapping data in the file and a bandwidth speed limiting execution mode. By adopting the system, the expandability can be improved.

A system receives, from a plurality of connected vehicles, respective GPS data indicating an absolute location of a respective connected vehicle and on-board sensor data indicating locations of nearby vehicles and objects relative to the respective connected vehicle. The system processes the data to create a shared-world model that includes at least the locations of the plurality of connected vehicles and the nearby vehicles and objects. Some implementations of the shared-world model further include the speeds and trajectories of each of the plurality of connected vehicles and the nearby vehicles and objects. The system transmits a representation of the shared-world model to one or more of the plurality of connected vehicles, which may utilize the shared-world model to provide advanced warnings for drivers or to provide improved path planning for autonomous vehicles. Some representations of the shared-world model include lane-level traffic functions such as traffic density, traffic speed, and traffic throughput.

The present invention discloses a traffic speed prediction method, device and storage medium based on ensemble learning and graph attention gated recurrent network. The method includes the following processes: obtaining traffic data source data, making traffic speed data set and sensor network adjacency matrix data; designing traffic speed prediction base model; using sensor historical data and adjacency matrix data to train the traffic speed prediction base model; using stack ensemble learning method, using gated recurrent neural network base model and graph attention gated recurrent neural network as base model, and using recurrent neural network as secondary model; training the secondary model module of recurrent neural network, designing loss function, and optimizing by optimization algorithm; using verification data set to test recurrent neural network performance index. The present invention predicts the accurate speed of roads in traffic network, improves the operation efficiency of urban traffic, reduces the management cost of traffic management department and improves people's travel experience.

The invention belongs to the technical field of intelligent traffic, and particularly relates to a traffic prediction system based on a historical track set. The method comprises the following steps of: collecting track data generated when a vehicle runs on a road network by using navigation positioning equipment, and constructing a hypergraph attention model based on a historical track set; the model is used for capturing space-time related characteristics on the hypergraph structure and the road network, so that the driving speed change of different roads in the future road network is accurately predicted; the system comprises a trajectory set feature extraction and enhancement module, a trajectory set hypergraph construction module, a hypergraph attention model module and a traffic prediction layer module. According to the method, heterogeneous and complex traffic semantic information is integrated by means of various space-time attention mechanisms; and meanwhile, a feature conversion relation between the road map and the track set hypergraph is established, so that features of a hypergraph space and a road network space are efficiently exchanged, and the future traffic speed is accurately predicted.

A traffic speed prediction apparatus may include: a communication device configured to receive probe data from a probe vehicle driving on a road; a storage configured to store data and algorithms for predicting a traffic speed; and at least one processor electrically connected to the communication device and the storage. The processor may be configured to predict the traffic speed on the road using traffic speed data, traffic volume data, and congestion data obtained based on probe data, and to obtain the congestion data by creating a target road network including a target collection section where the probe data is collected and determining a congestion matrix of the target road network.

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 multi-dimensional road network data integrated supervision system and method based on traffic service data, and relates to the technical field of multi-dimensional road network data supervision, and the method comprises the steps: constructing a road network supervision cloud platform, obtaining the historical vehicle passing record of a road section, obtaining the historical vehicle passing rate data from the historical vehicle passing record, and carrying out the management of the historical vehicle passing rate data. Performing traffic abnormality evaluation on road sections in the city; analyzing the abnormal influence degree of the traffic flow on the traffic of the characteristic road section to obtain characteristic vehicle traffic data; acquiring historical traffic data of historical public transport vehicles in the city, constructing a vehicle traffic prediction model in combination with the historical vehicle traffic data of the feature road section, and predicting the vehicle traffic condition of the feature road section in the current period; and acquiring passing data of public transport vehicles in the city in the current period, and performing intelligent supervision on the traffic of the feature road section in combination with the feature vehicle passing data of the feature road section.

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 discloses a traffic speed limit value determination method and device, equipment and a medium, and relates to the field of auxiliary driving, and the method comprises the steps: triggering the operation of obtaining a current first speed limit value and a current second speed limit value after a vehicle drives into an expressway; the current first speed limit value and the current second speed limit value are speed limit values determined based on a current road type and a current traffic signboard recognized by a forward-looking monocular camera of the vehicle respectively; when the current road type does not belong to the expressway and the current traffic signboard does not belong to the speed limit signboard, if the current first speed limit value is inconsistent with the current second speed limit value, whether the current road type is wrongly identified is determined by determining whether the current vehicle drives away from the expressway; and then the current target speed limit value is determined according to the judgment result and based on the current first speed limit value and the current second speed limit value, so that the vehicle is controlled to run according to the current target speed limit value, and the determination accuracy of the traffic speed limit value and the running safety of the vehicle are improved.

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 prediction method based on correlation learning of POI data and text information, and designs a plug-and-play POI use component for extracting intrinsic distribution features of the POI data according to sparsity and density differences of the POI data and traffic sequence data in a feature dimension and static and dynamic change differences in a time dimension. According to the method, a depth-breadth model thought of a recommendation system is used for reference, feature expression is enhanced by adding a gating mechanism to a prediction output layer, and a POI data and text information association learning network is introduced to measure the similarity between POI category semantic information and traffic sequence features, so that static POI data can reflect space-time characteristics. The method comprises the steps of 1, POI data and text information; step 2, establishing a graph convolution backbone network; step 3, establishing an associated learning network; 4, predicting and outputting; according to the method, the effect of an existing graph convolutional network prediction model is improved on traffic speed data sets of four cities in China.

The embodiment of the invention provides a bridge safety early warning method and device and electronic equipment, and the method comprises the steps: obtaining the amplitude information of a plurality of target point locations and the traffic flow information of a target bridge segment based on a preset wave beam, and enabling the traffic flow information to comprise the vehicle passing number information and / or the vehicle passing speed information; according to the amplitude information, continuous frame deflection track information is generated, according to the traffic flow information, continuous frame target statistical information is generated, and the continuous frame target statistical information comprises traffic quantity statistical information of continuous frame target vehicle types, continuous frame vehicle traffic speed statistical information and / or continuous frame traffic congestion degree statistical information; and according to the continuous frame deflection track information and / or continuous frame target statistical information, generating target bridge section safety early warning information. According to the invention, the artificial participation degree in the bridge monitoring process is reduced, the bridge monitoring cost is reduced, the bridge monitoring steps are simplified, and the bridge monitoring efficiency and precision are improved.

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 provides a traffic flow and charging station load prediction method based on deep learning, and aims to improve the precision of traffic flow prediction and accurately predict the load condition of an electric vehicle charging station. The method comprises the following steps of: firstly, predicting future traffic flow and traffic speed by adopting a space-time graph convolutional network model based on an attention mechanism; and secondly, based on the probability distribution of the daily travel distance of the vehicle, calculating the charging demand of the electric vehicle by using a hybrid model and an expectation maximization algorithm, and predicting the energy consumption of the electric vehicle through a polynomial regression model. And finally, based on the driving behaviors of the electric vehicles, an electric vehicle queuing model is constructed, the model comprehensively considers the behaviors of capacity limitation, overlong waiting time, intolerant leaving and the like of the charging station, and the load prediction of the charging station is calculated through a random Markov chain analysis method. The method can effectively capture the time-space characteristics of the traffic flow and the charging demand of the electric vehicle, and improves the accuracy of load prediction of the charging station.

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.

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 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 public space emergency evacuation prediction method and system based on traffic entropy, and relates to the technical field of public space emergency evacuation management, and the method comprises the steps: obtaining a local evacuation test region; obtaining the total number of current pedestrians and the current pedestrian type composition of the current evacuated crowd; determining a current evacuation pedestrian density, a current evacuation traffic entropy and a current evacuation difference type difference coefficient; acquiring a crowd evacuation mixed speed according to the current evacuation pedestrian density, the current evacuation traffic entropy and the current evacuation difference type different coefficient; and acquiring crowd evacuation movement duration according to the evacuation walking path and the walking speed of each current segment. According to the method, the current evacuation pedestrian density, the current evacuation traffic entropy, the current evacuation difference type different coefficient and other influence factors in the mixed traffic flow are fully considered to calculate the crowd evacuation movement duration, and the problem that the evacuation traffic state cannot be accurately described by the existing prediction of the pedestrian density is solved; and the evacuation movement duration is difficult to accurately calculate.

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 invention relates to the field of intelligent traffic, deep learning and artificial intelligence, in particular to a deep learning method for urban traffic node mode similarity cognition. Traffic speed data is organized into a graph data format consisting of a feature matrix and an adjacency matrix. The processed speed data are sent into a dynamic multi-graph constructor, an output dynamic adjacency matrix and a space adjacency matrix are sent into a multi-scale graph convolution module together, and the speed data are sent into the multi-scale graph convolution module after being subjected to expansion causal convolution processing in each space-time layer; and splicing from each space-time layer through jump connection to obtain a final result. And finally, analyzing the traffic mode similarity reflected by the dynamic multi-graph. According to the method, the space-time dependent features in the intelligent traffic scene can be extracted more efficiently, and the problem that the perceptual view in the graph convolutional network is limited and the problem that the long-term traffic mode is not considered sufficiently in traffic mode similarity analysis are solved.

The present invention discloses a method and system for calculating the deflection of an asphalt pavement based on a traffic speed deflectometer, which relates to the field of road engineering and non-destructive testing technology, including: collecting test data through TSD, and establishing a mechanical model of a typical asphalt pavement structure under different working conditions; determining the theoretical deflection slope distribution characteristics of a typical asphalt pavement structure under TSD load under different working conditions, and performing curve fitting; determining the zero-point response position of the theoretical deflection slope; calculating the deflection value of each sensor position by integral calculation based on a curve fitting method, and correcting the measured deflection slope; and comparing and evaluating the results. The present invention can improve the accuracy of the calculation of the deflection value, and solves the problems of systematic error and computational complexity existing in the traditional TSD measurement method by optimizing the deflection slope fitting curve, clarifying the deflection slope characteristics of the typical pavement structure, and setting the theoretical deflection slope zero-point position, thereby achieving a more scientific and efficient evaluation of road structure performance.

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.