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RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning

Summary: Online map matching framed as an Online MDP for efficient, incremental fusion of historical and streaming data. RL with a novel learning process and reward design, graph/RNN encodings for trajectory–road heterogeneity, and contrastive learning to align latent spaces, achieving state-of-the-art accuracy, speed, and robustness on three real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7325
Venue
SIGMOD
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,777 | 26.07%
DOI
10.1145/3725346

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BibTeX Citation

@inproceedings{chen_sigmod25,
        title = {{RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning}},
        author = {Chen, Minxiao and Yuan, Haitao and Jiang, Nan and Zheng, Zhihan and Wu, Sai and Zhou, Ao and Wang, Shangguang},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3725346},
        url = {https://dl.acm.org/doi/10.1145/3725346},
        year = {2025}
}

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