@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://preview.aclanthology.org/ingest-acl/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."
}