Smart Traffic Management System Using IOT and Machine Learning
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Date
2026-02-09
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Abstract
Urban traffic congestion continues to be one of the most critical problems in fast growing cities, leading to longer travel durations, increased fuel consumption, higher pollution levels, and daily stress for commuters. Conventional fixed cycle traffic lights are unable to respond to constantly changing road conditions, which results in poor traffic movement and frequent bottlenecks. To address these shortcomings, this project presents a smart traffic management solution that combines Internet of Things (IoT) technology, embedded hardware, machine learning (ML), and cloud computing to optimize signal control, improve safety, and reduce congestion in real time. The system collects live traffic data using IR sensors, ultrasonic sensors, and camera modules to detect vehicle count, presence, and classification.