🤖 AI Summary
Traditional traffic signal control struggles to adapt to dynamic demand and lacks interpretability. This work proposes a hierarchical control framework that integrates short-term traffic state prediction using LSTM, candidate phase generation, structured reasoning via a large language model (LLM), and safety-constrained action filtering. Notably, the LLM is employed for the first time as a high-level decision-support module under explicit safety constraints, rather than as a low-level controller. Extensive experiments across multiple scenarios in the SUMO simulation platform demonstrate that the proposed approach significantly improves traffic throughput while achieving zero constraint violations through the safety filter, thereby balancing performance and interpretability.
📝 Abstract
Traffic signal control is a critical task in intelligent transportation systems, yet conventional fixed-time and rule-based methods often struggle to adapt to dynamic traffic demand and provide limited decision interpretability. This study proposes an LLM-augmented traffic signal control framework that integrates LSTM-based short-term traffic state prediction, predictive phase selection, structured large language model reasoning, and safety-constrained action filtering. The LSTM module forecasts future queue length, waiting time, vehicle count, and lane occupancy based on recent intersection-level observations. A predictive controller then generates candidate signal actions, while the LLM module evaluates these actions using structured traffic-state inputs and produces congestion diagnoses, phase adjustment recommendations, and natural-language explanations. To ensure operational reliability, all LLM-generated recommendations are validated by a safety filter before execution. Simulation-based experiments in SUMO compare the proposed method with fixed-time control, rule-based control, and an LSTM-based predictive baseline under balanced demand, directional peak demand, and sudden surge scenarios. The results indicate that the proposed framework improves traffic efficiency, especially under dynamic and non-recurrent traffic conditions, while maintaining zero constraint violations after safety filtering. Overall, this study demonstrates that LLMs can enhance traffic signal control when used as constrained reasoning and decision-support modules rather than direct low-level controllers. Keywords: Intelligent Transportation Systems; Traffic Signal Control; Large Language Models; LSTM; Traffic State Prediction; Decision Support; Safety-Constrained Control; SUMO Simulation.