In modern cities, the bane of daily commute is often unpredictable traffic congestion. With the current infrastructure, traffic light timings are mostly static and don't adapt to real-time traffic conditions. This leads to unnecessary traffic congestion and wastage of time for the daily commuter. Our solution? An intelligent traffic light control system that alters its timings dynamically based on real-time traffic congestion.
Features
Using OpenCV: Using OpenCV, we analyze live camera feeds to assess the volume of traffic in each lane at an intersection.
Dynamic Traffic Light Timings: Neural network models predict the optimal traffic light durations to minimize congestion.
Adaptive Learning: Our model becomes more accurate over time as it learns from more data.
Scalable: Can be easily deployed across multiple intersections and traffic conditions.
Eco-Friendly: Reducing traffic congestion also means reducing the amount of time cars spend idling, leading to reduced carbon emissions.
