Graph Classification based on Trajectory Feature Graph and Self-attention Mechanism for Transportation Mode Recognition

Transportation mode recognition has always been an important task in trajectory data mining. Trajectories are essentially sequences of trajectory points, so many studies have chosen sequence structures for modeling trajectories. However, sequence models cannot capture the higher-order structural features in trajectory. In this context, we propose a novel graph model Trajectory Feature Graph (TF-Graph) for capturing trajectory features. Core words are usually extracted to express the main meaning of a sentence in the field of Natural Language Processing. Inspired by this, we define different types of nodes and edges in the TF-Graph. Combined with the multidimensional properties designed for nodes and edges, both the structural information and semantic information can be taken into account. Furthermore, we process the transportation mode recognition as a graph classification task. A feature encoding method incorporating the self-attention mechanism is used in the graph embedding. Finally, we conducted classification experiments using the trajectory dataset from the Microsoft Geo-Life project, focusing on seven transportation modes: walking, bike, bus, car, taxi, subway, and train. The classification average accuracy reached 84.29%. In addition, comparing with other transportation mode recognition methods, it is found that our method has higher classification accuracy and more prominent feature extraction ability.