FutureLight: An Efficient Future Traffic Data-Driven Reinforcement Learning Framework for Traffic Signal Controls
Summary: FutureLight is an RL framework for traffic-signal control that exploits future route data via a signal-aware, lane-level macroscopic simulator. Future-aware state/reward/value augmentation plus pruning improves control quality and accelerates training by over 30×. (summarized by gpt-5.6-luna on Aug 17 2026)
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Authors
- 1. Zizhuo Xu (Hong Kong University of Science and Technology)
- 2. Haolun Ma (Hong Kong University of Science and Technology)
- 3. Lei Li (Hong Kong University of Science and Technology)
- 4. Zhiyuan Wang (Hong Kong University of Science and Technology)
- 5. Yunjie Huang (Hong Kong University of Science and Technology)
- 6. Xiaofang Zhou (Hong Kong University of Science and Technology)
BibTeX Citation
@article{xu_vldb26,
title = {{FutureLight: An Efficient Future Traffic Data-Driven Reinforcement Learning Framework for Traffic Signal Controls}},
author = {Xu, Zizhuo and Ma, Haolun and Li, Lei and Wang, Zhiyuan and Huang, Yunjie and Zhou, Xiaofang},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {9},
pages = {2344--2357},
doi = {10.14778/3819518.3819555},
url = {https://doi.org/10.14778/3819518.3819555},
year = {2026}
}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,072 | BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks | 2024 | VLDB | 7.6794594e-05 |
| 11,263 | Continuous Lifelong Conflict-Aware AGV Routing with Kinematic Constraints | 2025 | VLDB | 4.9793485e-05 |
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