Design and Implementation of A Smart Traffic Light System for Efficient Traffic Regulation

Traffic congestion is a major problem worldwide and in particular for African big cities. It can lead to decreased productivity, increased CO
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emissions, and a negative impact on the economy. One solution to this problem is to rethink the design of the traditional traffic light system by using modern technologies to make it smarter. Therefore, this paper proposes a design and implementation of a smart traffic light system. The designed smart traffic management system uses image processing techniques to detect and keep track of vehicles on the road. By comparing the count of lanes in an intersection, the smart traffic management system is able to determine which lanes have a higher density of vehicles. Lanes with a higher density of vehicles are given priority, meaning they are given a longer green light to allow more vehicles to pass through. Lanes with a lower density of vehicles are given less priority, meaning they are given a shorter green light to prevent unnecessary delays. The designed system has also an additional feature where it detects emergency vehicles on the road. The proposed system is developed using Open CV Python and implemented on a Raspberry Pi 3 Model B+ to control the traffic lights. The performance evaluation and experimental assessment of the proposed smart system demonstrate its ability to accurately and reliably detect and prioritize emergency vehicles, detect and count other vehicles and implement lane prioritization, traffic timing, and balancing. Overall, the designed smart traffic management system has been found to be an effective tool that can aid in regulating traffic flow.