The idea focuses on developing smart traffic lights that adapt in real time to traffic conditions rather than relying on fixed schedules. Using sensors or camera-based vehicle detection, the system measures traffic density, waiting times, and flow patterns. Machine learning models (e.g., reinforcement learning) then optimize signal timing dynamically, prioritizing congested lanes or emergency vehicles when necessary. This results in lower waiting times, lowering CO2, increasing productivity, and decreasing the capacity needed for roads, allowing cities to focus on their green development strategies.

Waiting at a traffic light.