An LLM-augmented framework combining LSTM traffic prediction, structured LLM reasoning, and safety-constrained filtering improves simulated traffic efficiency under dynamic conditions with zero safety violations.
GMAN: A graph multi -attention network for traffic prediction
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LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support
An LLM-augmented framework combining LSTM traffic prediction, structured LLM reasoning, and safety-constrained filtering improves simulated traffic efficiency under dynamic conditions with zero safety violations.