关键词: 智能交通, 异常轨迹识别, 深度学习, 轨迹数据, 轨迹重建, 数据增强

Abstract:

The uncertainty and the existence of outliers of vehicle trajectory data makes it challenging to be used in the digital transportation systems. In this paper, we propose the LSTM-AE-Attention deep learning model for vehicle trajectory reconstruction and anomalous trajectory detection. To realize the data augmentation, the trajectory dataset is expanded in the sample size and sample type using the Generative Adversarial Networks and the Bezier Curve. The trajectory features are extracted by the Autoencoder and the Long Short-term Memory networks, and the trajectory reconstruction is completed. An anomaly detection model is then developed using the AE Pre-training and the Attention networks. The experiment has been performed based on a practical trajectory dataset. The results show that the proposed model produces several evaluation indicators that are better than the Support Vector Machines, Random Forest and Long Short-term Memory networks results. In the trajectory reconstruction section, the decision coefficient of the model is 0.968. In the anomaly detection part, the F1 score is 9.8% higher than the average score of the other models. The LSTM- AE- Attention model is an effective method for smoothing the vehicle trajectory and correcting abnormal trajectory.

Key words: intelligent transportation, trajectory anomaly detection, deep learning, trajectory data, trajectory reconstruction, data augmentation