Smart traffic optimization using image processing

Currently the traffic control system in place in our country is non-flexible and non-adaptive to the ever growing number of vehicles on the road. It does not take into account the changing density of traffic during the different hours of the day. Consequently the roads get congested frequently and intersections get blocked. Time and fuel, two highly important resources get wasted in this inefficient working of the present-day system. In this article, we propose a dynamic system that overcomes all these drawbacks. Our system uses cameras installed at the red lights and intersections to monitor the traffic dynamically. It then processes this information using image processing, computes the volume of the real time traffic, sets the timer of the signal accordingly. Simultaneously, it monitors if there is any scope of congestion at the intersection and adjusts the timer to prevent it. The entire system works autonomously and has a quick turnaround time, saving critical resources at every junction. The system also has the potential to adopt machine learning techniques in order to recognize the different emerging patterns of the future traffic and reach an optimal solution.