Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems

• URL: http://arxiv.org/abs/2508.02344v2
• Date: Wed, 22 Oct 2025 07:05:37 GMT
• Title: Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems
• Abstract summary: We introduce Traffic-R1, a foundation model with human-like reasoning for Traffic signal control (TSC)<n>Traffic-R1 offers three main advantages: zero-shot generalization, transferring unchanged to new road networks and out-of-distribution incidents.<n>In production, the model manages signals affecting over 55,000 drivers daily, reduces average queue lengths by more than 5%, and halves operator workload.
• Score: 38.08537395310505
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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. Our model is available at https://huggingface.co/Season998/Traffic-R1.

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