A distributed reinforcement learning system for traffic signal optimization using GPS-based vehicle route prediction.
This project explores a new approach to traffic signal control by combining:
- GPS-based route prediction to anticipate vehicle movements
- Reinforcement learning agents for adaptive signal timing
- Distributed learning across multiple intersections
- Real-time optimization with SUMO traffic simulation
The system learns vehicle routing patterns from historical GPS data and uses this knowledge to proactively adjust traffic signals.
