Structure and Position-Aware Graph Modeling for Trajectory Similarity Computation Over Road Networks

Peilun Yang, Hanchen Wang, Zhangyi Xu, Zhengping Qian, Yongheng Wang, Ying Zhang

Abstract: Trajectory similarity computation is critical to various spatial data-related applications. To date, many deep learning-based approaches have been proposed to approximate trajectory similarity. However, most of previous models focus on trajectories in Euclidean space, neglecting the information of road networks, which is an important prerequisite in many applications, such as traffic analytics, social recommendation. In this paper, we study the trajectory similarity learning over road networks. Different from previous task, trajectories over road networks contain richer and more complex information, e.g., the geographical and structure information of road networks. To this end, we propose SPGMT, a graph modeling based approach that leverages abundant structure and position information inherent in road networks for trajectory similarity learning. Particularly, our graph model learns informative node representations by simultaneously incorporating structure information of nodes from a local perspective and position information from a global perspective. This road network oriented module is the first proposal to learn from a broad context of graph topology. Afterwards, SPGMT designs a self-attention network and employs an LSTM to learn the sequential information from trajectories. We conduct experiments on real-life datasets to demonstrate the superiority of SPGMT in terms of effectiveness. Besides, additional study shows the flexibility and robustness of SPGMT.

External IDs:dblp:conf/icde/YangWXQWZ25