path-representation-learning

This paper proposes TraveL, a Transformer-based multi-view framework for learning distributional representations of paths in road networks, capturing varied traveler behaviors and regional correlations, and outperforming state-of-the-art methods in travel time estimation, path similarity, and destination prediction.