City Traffic Flow Visual Prediction Based on Digital Twin
Due to the complexity and dynamic nature of urban road network data,it is difficult to interpret the road correlation directly,and the uncertainty of direct connectivity also affects the prediction accuracy.To solve these problems,with taxi trajectory data and graph convolutional neural network,we propose an intelligent visual prediction framework for urban traffic flow based on digital twins.In order to improve the prediction accuracy,we create the spatio-temporal correlation graph of the road network based on the historical traffic data,and construct the ASTRG-GCN traffic flow prediction model of the spatio-temporal convolutional network.Through digital twin technology,we integrate dynamic traffic data and virtual three-dimensional traffic scenes to simulate traffic scenes in real time and provide decision support for urban traffic optimization.Finally,we design and implement the visual analysis framework of urban traffic flow,which enables users to analyze traffic operation situation efficiently.Experimental results show that the prediction accuracy of the proposed model is higher than that of the comparison algorithm on the two data sets.The visual analysis system of digital twin can realize the effect of traffic congestion identification,traffic scene simulation and traffic change comparison,and provide decision support for traffic planners.
digital twincity traffictrajectory dataflow predictionvisualization analysis
国家科技期刊平台
NSTL
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