7 results about "Floating car data" patented technology

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.

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.

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.

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.

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.

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.

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.