Spatial-Temporal Recurrent Neural Network for Anomalous Trajectories Detection

Aiming to improve the quality of taxi service and protect the interests in passengers, anomalous trajectory detection attracts increasing attention. Most of the existing methods concentrate on the coordinate information about trajectories and learn the similarities between anomalous trajectories from a large number of coordinate sequences. These methods ignore the relationship of spatial-temporal and ignore the particularity of the whole trajectory. Through data analysis, we find that there are significant differences between normal trajectories and anomalous trajectories in terms of spatial-temporal characteristic. Meanwhile Recurrent Neural Network can use trajectory embedding to capture the sequential information on the trajectory. Consequently, we propose an efficient method named Spatial-Temporal Recurrent Neural Network (ST-RNN) using coordinate sequence and spatial-temporal sequence. ST-RNN combines the advantages of the Recurrent Neural Network (RNN) in learning sequence information and adds attention mechanism to the RNN to improve the performance of the model. The application of Spatial-Temporal Laws in anomalous trajectory detection also achieves a positive influence. Several experiments on a real-world dataset demonstrate that the proposed ST-RNN achieves state-of-the-art performance in most cases.