Reducing Urban Traffic Congestion: A Road Reconstruction Framework Via Graph-enhanced Large Language Models

Urban traffic congestion remains a persistent challenge, primarily due to outdated road network infrastructures that fail to accommodate evolving transportation demands. Although traditional mitigation strategies such as traffic signal control and lane reconfiguration offer localized and short-term relief, they often lack the systemic capacity necessary for ensuring long-term urban mobility resilience. To support structured and future-oriented road reconstruction, there is an urgent need for intelligent planning tools capable of perceiving network topology, understanding traffic dynamics, and executing effective reasoning. In this study, we introduce RoadGPT , an innovative decision-making framework designed for intelligent road network reconstruction. RoadGPT positions a Large Language Model (LLM) as the central planning agent, integrating multi-source urban data and leveraging carefully constructed prompts to emulate expert-level reasoning. Through a fusion of semantic representation and structure-aware guidance, the LLM effectively identifies congestion bottlenecks and generates coherent and actionable structural intervention strategies. To enhance the LLM's capacity for spatial understanding, RoadGPT incorporates Graph Convolutional Networks (GCNs) to model the topology and connectivity of the urban road network. These GCN-based embeddings provide essential structural context, thereby improving decision quality and enabling system-level coherence. Furthermore, we formalize the decision-making workflow as a Markov Decision Process (MDP) and adopt a Reinforcement Learning (RL) optimization loop driven by continuous feedback from the SUMO microscopic traffic simulator. This interactive loop allows the model to iteratively refine its reconstruction policy based on key performance indicators such as average travel time, queue length, and reconstruction cost. We validate RoadGPT on five real-world urban datasets of varying scales, experimental results indicate that RoadGPT consistently outperforms state-of-the-art baselines.