AI Based Traffic Management System

Background: Increased traffic flow in growing cities causes congestion, higher fuel consumption, and frequent traffic jams. Traditional traffic management systems rely heavily on traffic police and fixed traffic light controls, which lack real-time adaptability. This study addresses the need for an efficient, intelligent traffic management solution to reduce congestion and optimize vehicle flow. Objective: The main objective of this study is to develop an Artificial Intelligence (AI)-based traffic management system using YOLO algorithms to detect vehicle density and dynamically adjust traffic signals. Methods: The system utilizes phone cameras to capture video of traffic lanes, and YOLO algorithms are employed for real-time object detection and density evaluation. Data collected from the cameras are processed using Python to determine the vehicle count in each lane. Based on this analysis, Arduino microcontrollers are programmed to prioritize traffic signals for lanes with higher vehicle density. The proof-of-concept implementation includes a prototype setup with two phone cameras, Arduino, and LEDs. Results: The system successfully detected vehicle density and adjusted traffic signals dynamically, demonstrating improved optimization of vehicle flow compared to traditional fixed-time signal systems. Real-time parallel processing ensured continuous monitoring and responsiveness to changing traffic conditions. Conclusions: The implementation of this AI-based traffic management system demonstrates significant potential to enhance traffic flow and reduce congestion. Future improvements could include scaling the system to larger networks and integrating additional sensors for better performance under varied environmental conditions.