Traffi
An intelligent traffic light signal optimization model designed to reduce congestion and vehicle idle times in the city of Trnava.
The Challenge
Fixed-timer signal light cycles struggle to adapt during peak transit hours in Trnava, leading to bottlenecked intersections, unnecessary idle emissions, and delayed public transport.
- Inflexible timer intervals during unpredictable traffic shifts
- Elevated CO2 emissions caused by excessive stop-and-go idling
- Lack of real-time adaptive flow management across intersections
AI-Driven Solution
Traffi models key municipal intersections as a multi-agent reinforcement learning environment, dynamically tuning signal lengths based on live simulated traffic density.
Adaptive Signal Timing
Dynamically alters green light durations to maximize vehicle throughput.
Emission Reduction
Minimizes stop times to directly decrease urban carbon footprint.
Technical Architecture
Core Language
Python 3
Primary language powering data processing, simulation logic, and model training.
ML Framework
PyTorch
Deep Neural Networks and Reinforcement Learning agents driving decision policies.
Network Simulation
Traffic Flow Modeling
Simulated traffic topology modeled on real-world Trnava road networks.
View on GitHub
Check out the model implementation, source code, and project documentation on GitHub.