In this project, we developed an AI-based policy for managing a 4-way signalized intersection using entropy-driven decision making.
By measuring the uncertainty (entropy) in future traffic states, our agent dynamically decides when to switch or hold traffic light phases to reduce congestion and minimize waiting times.
Compared to a random control approach, our method significantly improves traffic flow, demonstrating the potential of advanced AI models for adaptive urban traffic management.