Traffic light system

Every year, the average driver spends 50 hours just sitting at red lights, costing billions in wasted fuel and lost productivity.

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Smart Flow AI - Intelligent Traffic Management System Powered by AI

Millions of people spend precious time every day in traffic jam. We discovered that majority of traffic lights work on pre-defined time intervals and do not consider the number of waiting vehicles in front of it. This leads to long waiting lines, delayed services of emergency vehicles, accidents on the roads, and wastage of fuel. Motivated by these problems, we created **Smart Flow AI **that is an intelligent system to observe, analyze and act upon traffic scenarios.

The idea was straightforward: what if the traffic lights would act like a human being and decide on the spot what to do in accordance with current traffic situation? As the solution, we created Smart Flow AI as a Fourways intersection management system with the help of Artificial Intelligence, Computer Vision, and Machine Learning technologies. The system constantly observes the traffic with the help of CCTV cameras and analyses traffic with the use of YOLOv8 and OpenCV libraries to detect and count cars, buses, trucks, motorcycles, bicycles, and pedestrians in every lane. We aimed at making Smart Flow AI more advanced than conventional traffic management systems. As such, we have made use of emergency vehicle detection to detect ambulances, fire engines, and other kinds of police vehicles and automatically create a Green Corridor via signal priority. Furthermore, accident detection was added for identifying accidents or stationary vehicles and re-directing the traffic away from any kind of congestion. In order to further increase the efficiency of our system, we used traffic prediction based on historical traffic data, weather conditions, and time of the day. Moreover, pollution-aware optimization reduces the idling time of vehicles and, thus, decreases the carbon footprint.

Working on Smart Flow AI turned out to be quite exciting and challenging. Combining real-time computer vision with intelligent traffic signal control posed various difficulties and needed to be carefully designed and optimized. The simultaneous processing of several video streams, detection of emergency vehicles under diverse weather conditions, and dealing with the dependencies between YOLOv8, PyTorch, and Python became major hurdles. Yet, all those difficulties gave us valuable experience in computer vision, machine learning, backend development, and smart city solutions. From this experience, we found out that the capabilities of Artificial Intelligence have enough potential to help tackle the urban issues in the real world and enhance the lives of many people. Smart Flow AI is more than just an AI hackathon project; it is the idea of the future of smart transportation. In the future, we would like to expand this system using reinforcement learning traffic control, drone-based traffic surveillance, edge AI implementation on IoT, and multi-intersections optimization across the whole city.

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