Self-initiated · 4 months · Developer
Smart Adaptive Traffic Flow Optimisation
A deep-reinforcement-learning agent that retimes urban traffic signals in a simulated SUMO environment, instead of running them on a fixed cycle.
- Trained an agent-based RL model against the simulator to optimise signal timing.
- Processed camera feeds with OpenCV and CNNs for lane-level signals — better flow, travel time and vehicle distribution in test runs.
- Python
- Reinforcement Learning
- OpenCV
- SUMO
- CNNs
- TensorBoard