Learning Latent Road Correlations from Trajectories

A core component of the Intelligent Transportation System (ITS) is road network, which forms the most basic transport infrastructure, and becomes widely applied in many traffic applications. In most traffic models, the spatial representation of road network is learned only through static graph connection while dynamic driver preference and traffic conditions in the real world are ignored. Therefore, in this paper, a novel trajectory-based road network representation is proposed. By mining vehicle trajectories, our proposed method can learn dynamic route choice through embeddings of each road in a next-hop prediction model. Then road correlations are calculated by the embeddings to build a latent correlation graph that can be applied in various traffic-related applications. Extensive experiment results prove the effectiveness and rationality of our proposed approach.