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20 results about "Floating car data" patented technology
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Floating car data (FCD), also known as floating cellular data, is a method to determine the traffic speed on the road network. It is based on the collection of localization data, speed, direction of travel and time information from mobile phones in vehicles that are being driven. These data are the essential source for traffic information and for most intelligent transportation systems (ITS). This means that every vehicle with an active mobile phone acts as a sensor for the road network. Based on these data, traffic congestion can be identified, travel times can be calculated, and traffic reports can be rapidly generated. In contrast to traffic cameras, number plate recognition systems, and induction loops embedded in the roadway, no additional hardware on the road network is necessary.
Road traffic speed prediction method fusing multi-feature neural network
PCT designated stage expiredWO2024244300A1Internal combustion piston enginesDetection of traffic movementData setEngineering
The present invention relates to a road traffic speed prediction method fusing a multi-feature neural network, comprising: performing feature data pre-processing and using a data set to train a multi-feature input based MF-Informer traffic speed prediction model: input normalization of a multi-source traffic speed prediction model is realized by means of feature data pre-processing; and a data set is used to train a multi-feature input based MF-Informer traffic speed prediction model to implement prediction, wherein the MF-Informer traffic speed prediction model comprises a location information embedding module, an encoder module, and a decoder module. A prediction result is outputted by means of a fully connected layer, an error between the prediction result and a real value is calculated, reverse gradient propagation is performed, and the model is continuously optimized; finally, the trained model is used to perform traffic speed prediction on a prediction time period, the prediction result is outputted for denormalization to obtain output data, and traffic speed prediction model evaluation is completed. According to the present invention, multi-dimensional feature fusion is realized by combining floating vehicle data with meteorological data and air pollution data, thereby effectively improving the accuracy of traffic speed prediction.
Owner:JILIN INST OF CHEM TECH
Urban traffic intelligent regulation and control system and method based on digital twinning
The invention discloses an urban traffic intelligent regulation and control system and method based on digital twinning. The method comprises the following steps: S1, collecting and fusing multi-source data in real time; s2, constructing a digital twinborn model; s3, traffic flow prediction and optimization; s4, multi-system cooperative control is carried out; the system comprises the following modules: a multi-source data real-time acquisition and fusion module which comprises a video data acquisition and processing unit, a geomagnetic sensor network construction unit and a floating car data integration unit; the digital twinborn model building module comprises a three-dimensional road network model building unit and a data fusion and calibration unit; the traffic flow prediction and optimization module comprises an intelligent prediction model unit and a real-time optimization control unit; and the multi-system cooperative control module comprises an emergency priority passing system unit, a reversible lane dynamic adjustment unit and a parking lot induction unit. The traffic efficiency can be remarkably improved, the cooperative response speed is greatly increased, and carbon emission is effectively reduced.
Owner:YANTAI JIERUI NETWORK TRADING
A generalized estimation method and system for saturated headway at urban road signalized intersections
The present invention relates to the field of urban road traffic technology, and discloses a generalized estimation method and system for saturated headway at urban road signal intersections, comprising obtaining license plate recognition data and extracting an intersection signal cycle; matching the license plate recognition data with floating vehicle data, extracting saturated headway samples of vehicles queuing at the intersection under all intersection signal cycles; screening out factors influencing the saturated headway; using the saturated headway samples as input, and constructing a probability estimation model of the saturated headway and various saturated headway influencing factors based on the NGBoost model according to the saturated headway influencing factors; using each saturated headway influencing factor as input to the probability estimation model, and outputting a generalized estimation result of the saturated headway. The beneficial effect of the present invention is that it can generalize the saturated headway based on existing and easily obtainable license plate recognition data and floating vehicle data.
Owner:SOUTHEAST UNIV
Traffic traceability and flow statistics method based on hidden Markov
PendingCN120508582AData processing applicationsVisual data miningParallel optimizationTraffic system
The invention discloses a traffic traceability and flow statistics method based on hidden Markov, and belongs to the field of intelligent traffic. The method comprises the following steps: step 1, cleaning and standardizing floating car data; step 2, establishing a path matching model based on hidden Markov; step 3, carrying out parallel optimization on road network partitioning and path matching through multiple threads; and step 4, path traceability and flow analysis. The problems of low precision, poor efficiency and insufficient traceability in vehicle track traceability in the prior art are solved, spatial-temporal feature modeling, multi-thread parallel optimization and dynamic path backtracking technologies are fused, the traffic traceability and flow statistics method based on the hidden Markov model is provided, traffic traceability and flow statistics can be accurately achieved, and the traffic traceability and flow statistics efficiency are improved. And powerful support is provided for optimization of the intelligent traffic system.
Owner:BEIJING UNIV OF TECH
A method for estimating traffic flow of all regional road sections based on probe vehicle data
The present application belongs to the field of traffic flow estimation, and relates to a full-area road section traffic flow estimation method based on floating car data. The present application firstly classifies full-area road sections and determines reference road sections and non-reference road sections based on road section information investigation; then collects the flow of the reference road sections using radar, and collects the free flow speed and real-time average speed of the full-area road sections using the floating car method; finally, selects each reference road section, calibrates the jam density on the single lane of the road section based on the collected flow data and floating car data, and estimates the flow of the remaining non-reference road sections using the jam density and floating car data. The present application reduces the cost of flow data acquisition, reduces the workload and data storage of manual work, classifies the road sections in the research area according to the urban built environment, classifies and estimates the flow of the full-area road sections according to the classification results, and significantly improves the accuracy of flow estimation.
Owner:DALIAN UNIV OF TECH
Urban road signal intersection saturated time headway generalization estimation method and system
ActiveCN119132041AImprove generalization abilityImprove robustnessDetection of traffic movementData ingestionProbability estimation
The invention relates to the technical field of urban road traffic, and discloses an urban road signal intersection saturated time headway generalization estimation method and system, and the method comprises the steps: obtaining license plate recognition data, and carrying out the extraction to obtain an intersection signal period; matching the license plate identification data with the floating vehicle data, and extracting saturated time headway samples of vehicles queued at the intersection in all intersection signal periods; screening out saturated time headway influence factors; using the saturated time headway sample as input, and constructing a probability estimation model of the saturated time headway and various saturated time headway influence factors based on an NGBoost model according to the saturated time headway influence factors; and taking each saturated time headway influence factor as the input of the probability estimation model, and outputting a generalization estimation result of the saturated time headway. The method has the beneficial effects that generalization estimation can be carried out on the saturated time headway according to the existing number plate identification data and the floating car data which are easy to obtain.
Owner:SOUTHEAST UNIV
Road emergency analysis method and device
The invention discloses a highway emergency analysis method and device. A server side method comprises the following steps: determining a key monitoring area; based on the key monitoring area, establishing an adjacent relationship between each video point location and an adjacent portal frame corresponding to each video point location; obtaining an expressway roadside video of each video point location in a historical time period, obtaining expressway portal frame data of an adjacent portal frame corresponding to each video point location through an adjacent relation, and determining a high-value time period according to the expressway roadside video and the expressway portal frame data; floating car data on the highway section in the key monitoring area are obtained, abnormal clues are generated according to the traffic flow data or the floating car data in the high-value time period, the abnormal clues comprise traffic abnormal clues or speed abnormal clues, and the clues are sent to a client side to judge emergencies. Therefore, by adopting the embodiment of the invention, the recognition capability of emergencies can be improved, and differentiated and targeted monitoring of high-risk areas can be realized.
Owner:BEIJING PALMGO INFOTECH CO LTD
Road abnormal event monitoring system based on video image analysis
The invention discloses a road abnormal event monitoring system based on video image analysis, and relates to the technical field of intelligent traffic, and the system comprises a video stream access module, a vehicle behavior data access module, a core processing module and an output module, the vehicle behavior data access module anonymously receives floating vehicle data; the core processing module is deployed in a roadside edge calculation unit, maps floating car data to an image coordinate system through space-time alignment, generates a behavior thermodynamic diagram, guides video image feature enhancement, optimizes the thermodynamic diagram through enhanced features, and finally outputs an abnormal event and confidence through collaborative decision logic; according to the invention, the deep fusion and mutual enhancement of the video data and the vehicle behavior data are realized, the real-time performance and the robustness are realized, the working modes can be automatically switched, the existing traffic resources are fully utilized, and the deployment cost is reduced.
Owner:YONG ZHONG GONG CHENG GUAN LI (JI TUAN) YOU XIAN GONG SI +3
Traffic pressure dispersion method comprehensively applying intelligent technology and dynamic strategy
PendingCN122050136AControlling traffic signalsDetection of traffic movementExtreme weatherSimulation
The invention discloses a traffic pressure dispersion method comprehensively applying an intelligent technology and a dynamic strategy. The method comprises the following steps: 1, fusing radar, video and floating car data by utilizing an improved Dempster-Shafer evidence theory, and solving the problems of sensing conflict and distortion of a single sensor in severe weather in combination with a historical credibility correction mechanism; 2, constructing a multi-dimensional dynamic traffic pressure index containing physical congestion and psychological congestion; 3, establishing a layered multi-agent reinforcement learning architecture, outputting a mixed action vector by a lower-layer agent, and synchronously and dynamically adjusting signal lamp timing and tide variable lane functions; and 4, mapping the control strategy into an NTCIP 1202 standard protocol object in real time, and issuing the NTCIP 1202 standard protocol object to the existing signal control equipment through an SNMP instruction. According to the method, the limitation of space-time splitting of a traditional control means is effectively broken through, and the passing efficiency and robustness of a complex road network in extreme weather and tidal flow scenes are remarkably improved.
Owner:DEZHOU UNIV
A floating vehicle data trajectory restoration method based on hidden Markov model
The present invention proposes a floating vehicle data trajectory restoration method based on a hidden Markov model. Starting from the spatiotemporal characteristics of floating vehicle data and urban road data, the present invention fills in the information of the floating vehicle's route while matching the floating vehicle data to the urban road. Specifically, the present invention has the following innovations: the length of the shortest path between all current candidate point pairs is considered in the calculation of any transition probability of the hidden Markov model; the point-line relationship function is introduced to screen all candidate points on each candidate road, and used to supplement the observation probability of the hidden Markov model; the shortest path between candidate point pairs and the length of the straight line between the corresponding original trajectory points are considered again outside the hidden Markov model to avoid the probability model ignoring the consideration of connectivity when other probabilities are larger. The method proposed by the present invention can efficiently and accurately restore the driving trajectory of the floating vehicle, thereby improving the sampling frequency and value of the floating vehicle data.
Owner:SHENZHEN DIGITAL CITY ENG RES CENT +1
Traffic flow prediction method based on floating car data and mutual information topology reconstruction
PendingCN121998163Aimprove accuracybuild accuratelyDetection of traffic movementForecastingTraffic forecastTraffic network
The invention provides a traffic flow prediction method based on floating car data and mutual information topology reconstruction, and the method comprises the steps: obtaining non-full-sample floating car trajectory data, and generating a traffic flow time sequence of each road node; based on the traffic flow time sequence of each road node, a mutual information value between any two road nodes is calculated, an adjacent matrix is constructed according to the mutual information values, and elements of the adjacent matrix represent a dependency relationship between the corresponding nodes; a traffic network diagram is constructed according to the adjacency matrix, the traffic network diagram comprises a node set, an edge set and the adjacency matrix, the node set represents road nodes, and the edge set represents connectivity between the nodes; taking the traffic network graph and the traffic flow time sequence as input, and training a traffic flow prediction model by using a graph neural network model; and the trained traffic flow prediction model is utilized to predict the traffic flow in the future time period and output the prediction result, so that the accuracy of traffic flow prediction is effectively improved.
Owner:BEIJING EASY TIMES DIGITAL TECH
Intelligent traffic path optimization method and device, electronic equipment and readable storage medium
The invention provides an intelligent traffic path optimization method and device, electronic equipment and a readable storage medium. The method comprises the following steps: converting link and node attributes of a real road network into road attribute embedding characteristics through an embedding matrix; performing multi-level processing on the features by using a relational graph convolutional network and a gating loop unit, and extracting spatial-temporal features of the road network; generating an initial transition probability and a candidate path set on the basis of the starting point-ending point pair data, and screening out an optimal path consistent with the observed driving time and the estimated driving time of the optimal path; and estimating driving time distribution parameters and transition probabilities of the road sections and the intersections, generating an optimized candidate path set, and updating model parameters. The method combines weak supervised learning and deep learning technologies, utilizes large-scale floating vehicle data, captures global and local features of a road network, provides more accurate driving time estimation, and is suitable for an intelligent traffic system and traffic management.
Owner:JILIN UNIVERSITY
Road incident analysis method and device
ActiveCN119889087BAccurate monitoringaccurate identificationDetection of traffic movementAnti-collision systemsSimulationOperations research
The application discloses a highway emergency event analysis method and device, a server method comprising: determining a key monitoring area; based on the key monitoring area, establishing an adjacent relationship between each video point and adjacent gantries corresponding to each video point; obtaining highway roadside videos of each video point in a historical time period, obtaining highway gantry data of adjacent gantries corresponding to each video point through the adjacent relationship, and determining a high-value time period according to the highway roadside videos and the highway gantry data; obtaining floating car data on a highway section in the key monitoring area, generating an abnormal clue according to traffic flow data or the floating car data in the high-value time period, the abnormal clue comprising a flow abnormal clue or a speed abnormal clue, and sending the clue to a client to determine an emergency event. Therefore, the application can improve the identification ability of the emergency event and realize differentiated and targeted monitoring of a high-risk area.
Owner:BEIJING PALMGO INFOTECH CO LTD
Method and program product for identifying spatiotemporal location of traffic accidents using floating car data
Owner:NANJING UNIV
Road abnormal event monitoring system based on video image analysis
The application discloses a highway abnormal event monitoring system based on video image analysis and relates to the technical field of intelligent transportation.The system comprises a video stream access module, a vehicle behavior data access module, a core processing module and an output module.The video stream access module acquires real-time video stream, and the vehicle behavior data access module anonymously receives floating car data.The core processing module is arranged on a roadside edge computing unit, floating car data is mapped to an image coordinate system through space-time alignment, a behavior heat map is generated, video image feature enhancement is guided, the heat map is optimized through enhanced features, and finally abnormal events and confidence are output through collaborative determination logic.The output module uploads high-confidence abnormal event information to a cloud management platform.The application realizes deep fusion and mutual enhancement of video data and vehicle behavior data, has real-time performance and robustness, can automatically switch working modes, fully utilizes existing traffic resources and reduces deployment costs.
Owner:YONG ZHONG GONG CHENG GUAN LI (JI TUAN) YOU XIAN GONG SI +3
A method for identifying vehicle operation behavior types based on sparse trajectories
ActiveCN114091581BGeographical information databasesSpecial data processing applicationsAlgorithmEngineering
The present invention discloses a method for identifying vehicle operation behavior types based on sparse trajectories. The method steps are as follows: removing invalid data from multi-type vehicle trajectory data through data cleaning to obtain the cleaned vehicle trajectory data; performing data fusion on the cleaned vehicle trajectory data and semantic segmentation data to obtain the fused data; sparsely sampling the trajectory segment data based on monitoring points; training a deep network for travel type classification; inputting a sparse trajectory sequence to be judged, and outputting the driving category distribution of the trajectory segment. The beneficial effects of the present invention are as follows: The present invention can fuse floating car data and multi-source data such as private cars for analysis, providing more insights into vehicle movement behavior patterns and enabling a better understanding of the urban traffic state; it provides the possibility of discovering vehicle movement patterns and offers a new perspective for distinguishing vehicles used for illegal operation.
Owner:WUHAN YANGTZE COMM ZHILIAN TECH +1
A method and system for estimating queue length at signalized intersections based on floating vehicle data
ActiveCN118865681BInternal combustion piston enginesDetection of traffic movementData ingestionSimulation
This invention proposes a method and system for estimating queue lengths at signalized intersections based on floating vehicle data. The method first performs map matching on low-frequency floating vehicle data. Next, a relationship model is constructed between the periodic maximum queue length and the spatial distribution of floating vehicles. Based on historical floating vehicle data, the mean of the periodic maximum queue is estimated. Then, based on the historical floating vehicle data and intersection signal timing data, the periodic average maximum queue length of floating vehicle samples is extracted and the variance is obtained. Finally, the obtained mean and variance are used to define a prior distribution of the periodic maximum queue length. Based on the floating vehicle data observed during the current period, a Bayesian approach is used to estimate the posterior distribution of the periodic queue length during the current period. This invention utilizes low-frequency, low-penetration floating vehicle data, which is widely available in real urban road networks, to achieve widespread and applicable acquisition of queue status at signalized intersections in urban road networks, providing fundamental support for urban road network traffic management and control.
Owner:SOUTHEAST UNIV
A method for predicting and extrapolating the operational status of a channel based on deep fusion of multi-source information
This invention discloses a method for predicting and extrapolating the operational status of a transportation corridor based on deep fusion of multi-source information. The method includes: processing data collected by geomagnetic detectors deployed along a road segment to generate traffic flow and speed information for the nodes of that segment; preprocessing floating car data from the road segment; preprocessing meteorological and geological disaster information; constructing a dynamic graph neural network based on the characteristics of the geomagnetic detectors and floating car data to capture the temporal and spatial characteristics of the traffic data; constructing a knowledge graph based on meteorological and geological disaster information to characterize the characteristics of extreme environments along the corridor; encoding the knowledge graph and inputting it into the graph neural network model to predict indicators characterizing the operational status of the corridor. This invention, by employing a multi-source data fusion method, characterizes and extrapolates the traffic situation of a corridor, providing a methodology for studying comprehensive emergency response technologies for transportation under complex disasters in extreme environments.
Owner:SOUTHEAST UNIV
A road traffic anomaly detection method based on holomorphic embedding and global search
ActiveCN119007450Bquality improvementAccurate detectionInternal combustion piston enginesDetection of traffic movementAlternating least squaresLocal optimum
The present invention provides a road traffic anomaly detection method based on holomorphic embedding and global search, including obtaining floating car data; defining specific road sections and calculating average speeds; constructing an urban road traffic network; constructing an average speed transfer matrix according to the probability distribution of speed conversion; constructing a tensor model according to the traffic data to be detected; using trust technology to perform global search to find a local optimal solution that meets error conditions; calculating the Kullback-Leibler divergence value of the speed transfer matrix and the average speed transfer matrix to detect anomalies in the urban road traffic network. The present invention can detect road traffic anomalies more accurately and efficiently, improve traffic management efficiency and traffic network effectiveness; and overcome the defects of the alternating least squares method, such as slow convergence, low computational efficiency, low precision, and inability to process CP decomposition of tensors containing many zero elements.
Owner:SUN YAT SEN UNIV
Vehicle matching method and apparatus, device, storage medium, and computer program product
PendingEP4633206A1Road vehicles traffic controlParticular environment based servicesSensing dataGeographic regions
A vehicle matching method is provided, including: obtaining a first current frame of floating car data of a first vehicle (202); determining a candidate geographic region based on a geographic location from the first current frame of floating car data, and obtaining a second current frame of candidate vehicle sensing data of each candidate vehicle of at least one candidate vehicle in the candidate geographic region (204); respectively calculating a relative location error degree between the first vehicle and the each candidate vehicle based on the first current frame of floating car data and the second current frame of candidate vehicle sensing data of the respective candidate vehicle (206); respectively calculating a matching confidence between the first vehicle and the respective candidate vehicle based on the relative location error degree (208); selecting, from the at least one candidate vehicle, a second vehicle that satisfies a pre-defined error-degree threshold condition and / or a confidence threshold condition (210, 212).
Owner:TENCENT TECHNOLOGY (SHENZHEN) CO LTD