Road Network Representation Learning with Vehicle Trajectories

Abstract

Spatio-temporal traffic patterns reflecting the mobility behavior of road users are essential for learning effective general-purpose road representations. Such patterns are largely neglected in state-of-the-art road representation learning, mainly focusing on modeling road topology and static road features. Incorporating traffic patterns into road network representation learning is particularly challenging due to the complex relationship between road network structure and mobility behavior of road users. In this paper, we present TrajRNE – a novel trajectory-based road embedding model incorporating vehicle trajectory information into road network representation learning. Our experiments on two real-world datasets demonstrate that TrajRNE outperforms state-of-the-art road representation learning baselines on various downstream tasks.