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Design and Implementation of Smart Traffic Control System Based on Traffic Density

Authors : Harshavardhan Bhosale Patil, Rajvardhan Desai, Vipul Mengane, Parth Dokare

Abstract- With the rapid growth of urban populations and the increasing number of vehicles on the road, traffic congestion has become a significant problem in metropolitan areas. Conventional traffic control systems, which rely on pre-set signal timers, often fail to address real-time traffic conditions effectively, leading to inefficient traffic flow, increased fuel consumption, and environmental degradation. The paper “Smart Traffic Control System Based on Traffic Density Using YOLO” introduces a novel approach to address this issue by integrating artificial intelligence and computer vision into traffic management.

The proposed system utilizes the YOLO (You Only Look Once) object detection algorithm to detect and classify vehicles in real-time from live video feeds captured at intersections. YOLO, known for its speed and accuracy in object detection tasks, calculates the vehicle density in each direction. Based on the detected density, the system dynamically adjusts the green light duration for each lane, ensuring that the direction with higher traffic receives longer green light periods. This method aims to optimize signal switching and minimize overall vehicle wait time.

The paper outlines the complete architecture of the system, which includes modules for video input processing, vehicle detection using YOLO, traffic density computation, and adaptive signal timing control. By leveraging deep learning, the system enhances decision-making capabilities without relying on costly hardware like inductive loops or infrared sensors. This review evaluates the effectiveness and innovation of the proposed system, comparing it with traditional and sensor-based models. It highlights the advantages of using YOLO, such as real-time processing, high accuracy, and scalability. Additionally, the review discusses the system’s limitations, such as dependency on video clarity and environmental conditions, and proposes areas for future enhancement, including integration with cloud computing and edge AI for broader deployment.

Keywords- Smart Traffic Control System, YOLO Object Detection, Traffic Density Estimation, Real-Time Traffic Monitoring, Adaptive Traffic Signal Control, Vehicle Classification ( Cars, Bikes, Buses, Trucks, Rikshaws), Intelligent Transportation System.