The congestion of urban traffic is becoming one of the critical issues with increasing population and automobiles in cities. Traffic jams not only cause extra delay and stress for the drivers, but also increase fuel consumption, add transportation costs, and increase air pollution. Although it seems to pervade everywhere, megacities are the ones most affected by it. And it's ever increasing nature makes it imperative to know the road traffic density in realtime for better signal control and effective traffic management. The traffic controller is one of the critical factors affecting traffic flow. The current traffic management systems that are in place are generally static, which means that they do not adjust according to the needs of the traffic flow. Our proposed system aims to design a traffic light controller based on Computer Vision that can adapt to the current traffic situation. It uses live video feed from the CCTV cameras at traffic junctions for realtime traffic density calculation by detecting the vehicles at the signal and setting the green signal time accordingly. The vehicles are classified as car, bike, bus/truck, or rickshaw to obtain a more accurate estimate of the green signal time. We have used object detection techniques like YOLO in order to detect the number of vehicles for each direction. We then set the timers of these traffic signals according to vehicle density in each direction and hence the system becomes adaptive. This helps to optimize the green signal times, and traffic is cleared at a much faster rate than a static system, thus reducing the unwanted delays, congestion, and waiting time, which in turn will reduce the fuel consumption and pollution.

Source Code: https://github.com/mihirmgandhi/Ada...
Research Paper: https://ieeexplore.ieee.org/document/...

Email: [email protected]
LinkedIn:   / mihirmgandhi  

#gpmcoonlineproject