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The vehicle license plate data obtained from video-imaging detectors contains a huge volume of information of vehicle trip rules and driving behavior characteristics. In this paper, a real-time vehicle trajectory prediction method is proposed based on historical trip rules extracted from vehicle license plate data in an urban road environment. Using the driving status information at intersections, the vehicle trip chain is acquired on the basis of the topologic graph of the road network and channelization of intersections. In order to obtain an integral and continuous trip chain in cases where data is missing in the original vehicle license plate, a trip chain compensation method based on the Dijkstra algorithm is presented. Moreover, the turning state transition matrix which is used to describe the turning probability of a vehicle when it passes a certain intersection is calculated by a massive volume of historical trip chain data. Finally, a k -step vehicle trajectory prediction model is proposed to obtain the maximum possibility of downstream intersections. The overall method is thoroughly tested and demonstrated in a realistic road traffic scenario with actual vehicle license plate data. The results show that vehicles can reach an average accuracy of 0.72 for one-step prediction when there are only 200 historical training data samples. The proposed method presents significant performance in trajectory prediction.

Vehicle Trajectory Prediction Method Based on License Plate Information Obtained from Video-Imaging Detectors in Urban Road Environment