IoT-Based Smart Traffic Management System using Computer Vision and Deep Learning

Abstract

Urban traffic congestion is a critical challenge worldwide, leading to increased travel time, pollution, and economic losses. This paper presents an IoT-based smart traffic management system leveraging computer vision and deep learning to enhance traffic flow and reduce congestion. The system integrates real-time video data from roadside cameras with IoT devices to monitor traffic conditions continuously. Computer vision algorithms detect and classify vehicles, pedestrians, and traffic signals, while deep learning models predict traffic patterns and optimize signal timings adaptively. The use of convolutional neural networks (CNNs) allows accurate recognition of various vehicle types and detection of traffic violations, enabling dynamic response to traffic conditions. Data collected through IoT sensors is processed using edge computing to minimize latency and improve responsiveness. The system supports features such as emergency vehicle prioritization, accident detection, and real-time traffic density estimation. Extensive testing on urban road networks demonstrates significant improvements in traffic throughput, reduced waiting times, and enhanced safety. The combination of IoT and AI technologies presents a scalable and efficient solution for modern smart cities aiming to tackle traffic management challenges. This research contributes to sustainable urban mobility by providing a framework that can be integrated with existing infrastructure, supporting future advancements such as autonomous vehicles and smart public transportation. Challenges related to data privacy, system scalability, and environmental variability are also discussed, highlighting areas for future improvement.