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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
7263
Venue
SIGMOD
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,502 | 26.94%
DOI
10.1145/3725346

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Rank Citing Paper Year Venue Pagerank
10,083 GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases 2026 SIGMOD 4.1945683e-05
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