Road Network Representation Learning with Vehicle Trajectories

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.

  • Published in:
    Pacific-Asia Conference on Knowledge Discovery and Data Mining
  • Type:
    Inproceedings
  • Authors:
    Schestakov, Stefan; Heinemeyer, Paul; Demidova, Elena
  • Year:
    2023

Citation information

Schestakov, Stefan; Heinemeyer, Paul; Demidova, Elena: Road Network Representation Learning with Vehicle Trajectories, Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2023, https://link.springer.com/chapter/10.1007/978-3-031-33383-5_5, Schestakov.etal.2023a,

Associated Lamarr Researchers

lamarr institute person demidova elena e1663924269458 - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Elena Demidova

Principal Investigator Hybrid ML to the profile