Vehicle Destination Prediction Based on Trajectory Data

Abstract

With the increasing requirements of real-time traffic management in urban transportation, vehicle destination prediction data, as an important part of smart city brain data, is becoming more and more important in urban traffic management and operation. This paper builds a vehicle destination prediction scheme based on trajectory data. By collecting public vehicle trajectory datasets, the Sequence to Sequence (Seq2Seq) sequence generation model and the Text-Convolutional Neural Network (Text-CNN) sequence classification model are built respectively to implement the vehicle destination prediction. In order to evaluate the performance of the models, Accuracy and Mean Distance are used as metrics to compare the model performance under different training data conditions. Advantage analysis was done in combination with different application scenarios for practical application reference. According to the prediction performance of different models built in this work in different application scenarios, the advantages are analyzed for reference in practice.