LLM-Augmented Traffic Signal Control with LSTM Prediction and Safety Constraints
A recent study published on arXiv (2604.23902) introduces a traffic signal control framework enhanced by an LLM. This system combines LSTM-based predictions for short-term traffic conditions, predictive phase selection, structured reasoning from a large language model, and safety-focused action filtering. The LSTM component anticipates future metrics such as queue length, waiting times, vehicle numbers, and lane usage based on current intersection data. A predictive controller proposes potential signal actions, which the LLM assesses through structured traffic-state information, yielding diagnoses of congestion, suggestions for phase adjustments, and explanations in natural language. All recommendations generated by the LLM undergo validation to ensure operational reliability.
Key facts
- arXiv paper 2604.23902 proposes LLM-augmented traffic signal control
- Integrates LSTM-based traffic state prediction
- LSTM forecasts queue length, waiting time, vehicle count, lane occupancy
- Predictive controller generates candidate signal actions
- LLM module evaluates actions with structured inputs
- LLM produces congestion diagnoses and phase adjustment recommendations
- LLM outputs natural-language explanations
- Safety-constrained action filtering ensures operational reliability
Entities
Institutions
- arXiv