RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning
Summary: RLER-TTE introduces a Decision Maker–Predictor pipeline for ER-TTE, triggering the expensive predictor only when beneficial. An RL-based online agent solves the MDP with attention-based spatio-temporal encoding and curriculum learning, delivering end-to-end training and strong real-world performance. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Zhihan Zheng (Beijing Institute of Technology)
- 2. Haitao Yuan (Nanyang Technological University)
- 3. Minxiao Chen (Beijing Institute of Technology)
- 4. Shangguang Wang (Beijing Institute of Technology)
BibTeX Citation
@inproceedings{zheng_sigmod25,
title = {{RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning}},
author = {Zheng, Zhihan and Yuan, Haitao and Chen, Minxiao and Wang, Shangguang},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709721},
url = {https://dl.acm.org/doi/10.1145/3709721},
year = {2025}
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,249 | GoodTP: An Effective Data Selection Framework for Enhancing Trajectory Similarity Learning via Monte Carlo Tree Search | 2026 | SIGMOD | 5.093636e-05 |
| 10,374 | GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases | 2026 | SIGMOD | 5.093636e-05 |
| 10,777 | RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning | 2025 | SIGMOD | 5.093636e-05 |
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