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)
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Authors
- 1. Minxiao Chen
- 2. Haitao Yuan
- 3. Nan Jiang
- 4. Zhihan Zheng
- 5. Sai Wu
- 6. Ao Zhou
- 7. Shangguang Wang
Incoming Citations (Sorted by Pagerank)
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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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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,388 | Anytime Stochastic Routing with Hybrid Learning | 2020 | VLDB | 8.9132902e-05 |
| 3,447 | Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter | 2020 | SIGMOD | 7.0854131e-05 |
| 10,390 | RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning | 2025 | SIGMOD | 4.1945683e-05 |
| 11,276 | Route Travel Time Estimation on A Road Network Revisited: Heterogeneity, Proximity, Periodicity and Dynamicity | 2023 | VLDB | 4.1945683e-05 |
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