@inproceedings{zou-etal-2026-traffic,
title = "Traffic-R1: Reinforced {LLM}s Bring Human-Like Reasoning to Traffic Signal Control Systems",
author = "Zou, Xingchen and
Yang, Yuhao and
Chen, Zheng and
Hao, Xixuan and
Chen, Yiqi and
Huang, Chao and
Liang, Yuxuan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.995/",
pages = "21823--21838",
ISBN = "979-8-89176-390-6",
abstract = "We introduce Traffic-R1, a 3B-parameter foundation model with human-like reasoning for Traffic signal control (TSC), developed via self-exploration and iterative reinforcement of LLM with expert guidance in a simulated traffic environment. Compared with traditional reinforcement learning and recent LLM-based methods, Traffic-R1 offers three main advantages: zero-shot generalization, transferring unchanged to new road networks and out-of-distribution incidents by leveraging internal traffic-control policies and reasoning; a compact 3B-parameter design that supports real-time inference on mobile-class chips for edge deployment; and an explainable TSC process that enables multi-intersection coordination through communication and an asynchronous communication network. Extensive benchmarks show Traffic-R1 outperforms strong baselines and training-intensive RL controllers. In production, the model now manages signals affecting over 55,000 drivers daily, reduces average queue lengths by more than 5{\%}, and halves operator workload. We will open source our checkpoint and code to foster further research."
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%0 Conference Proceedings
%T Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems
%A Zou, Xingchen
%A Yang, Yuhao
%A Chen, Zheng
%A Hao, Xixuan
%A Chen, Yiqi
%A Huang, Chao
%A Liang, Yuxuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zou-etal-2026-traffic
%X We introduce Traffic-R1, a 3B-parameter foundation model with human-like reasoning for Traffic signal control (TSC), developed via self-exploration and iterative reinforcement of LLM with expert guidance in a simulated traffic environment. Compared with traditional reinforcement learning and recent LLM-based methods, Traffic-R1 offers three main advantages: zero-shot generalization, transferring unchanged to new road networks and out-of-distribution incidents by leveraging internal traffic-control policies and reasoning; a compact 3B-parameter design that supports real-time inference on mobile-class chips for edge deployment; and an explainable TSC process that enables multi-intersection coordination through communication and an asynchronous communication network. Extensive benchmarks show Traffic-R1 outperforms strong baselines and training-intensive RL controllers. In production, the model now manages signals affecting over 55,000 drivers daily, reduces average queue lengths by more than 5%, and halves operator workload. We will open source our checkpoint and code to foster further research.
%U https://aclanthology.org/2026.acl-long.995/
%P 21823-21838
Markdown (Informal)
[Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems](https://aclanthology.org/2026.acl-long.995/) (Zou et al., ACL 2026)
ACL
- Xingchen Zou, Yuhao Yang, Zheng Chen, Xixuan Hao, Yiqi Chen, Chao Huang, and Yuxuan Liang. 2026. Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21823–21838, San Diego, California, United States. Association for Computational Linguistics.