Update README.md

README.md

@@ -22,6 +22,12 @@ This model is a checkpoint based on the research in our paper:
 **Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems** 📄
 [https://arxiv.org/abs/2508.02344](https://arxiv.org/abs/2508.02344)
 
+## Introduction Video
+
+<video controls>
+  <source src="Traffic-R1.mp4" type="video/mp4">
+</video>
+
 ## Abstract
 Traffic signal control (TSC) is vital for mitigating congestion and sustaining urban mobility. In this paper, we introduce Traffic-R1, a foundation model with human-like reasoning for TSC systems. Our model is developed through self-exploration and iteration of reinforced large language models (LLMs) with expert guidance in a simulated traffic environment. Compared to traditional reinforcement learning (RL) and recent LLM-based methods, Traffic-R1 offers three significant advantages. First, Traffic-R1 delivers zero-shot generalisation, transferring unchanged to new road networks and out-of-distribution incidents by utilizing its internal traffic control policies and human-like reasoning. Second, its 3B-parameter architecture is lightweight enough for real-time inference on mobile-class chips, enabling large-scale edge deployment. Third, Traffic-R1 provides an explainable TSC process and facilitates multi-intersection communication through its self-iteration and a new synchronous communication network. Extensive benchmarks demonstrate that Traffic-R1 sets a new state of the art, outperforming strong baselines and training-intensive RL controllers. In practice, the model now manages signals for more than 55,000 drivers daily, shortening average queues by over 5% and halving operator workload.