What is it about?

This paper explores how artificial intelligence (AI) and smart traffic control systems can make urban traffic management more efficient. Traffic in fast-growing cities often slows down due to outdated systems like fixed traffic lights and manual enforcement, which can’t adapt to changing traffic patterns. Using cameras with advanced deep learning models, such as YOLO, at key lane intersections, authorities can monitor vehicles, spot rule-breakers, and change traffic lights based on real-time conditions. This helps reduce congestion and improve traffic flow. Additionally, smart systems with machine learning can automatically adjust traffic signals, making roads less congested, reducing waiting time, and making travel smoother for everyone.

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Why is it important?

This work addresses a critical need for efficient traffic management in fast-growing urban areas. Traditional methods fall short due to the high costs of managing traffic, increased pollution, and the health impacts on commuters and traffic police. Our approach looks at the various AI-based traffic management systems to reduce congestion, automate traffic rule enforcement and violation detection, and adjust signal timings based on real-time traffic conditions. This not only improves urban mobility and cuts down economic losses but also enhances sustainability by reducing pollution and human involvement in hazardous traffic environments.

Perspectives

This research brings together modern technology and everyday challenges faced by people in urban areas. By implementing adaptive traffic management, we can create systems that respond to real-world situations on the road. With an interest in making cities more efficient, we aim to bridge the gap between advanced research and practical solutions that can positively impact city infrastructure and residents’ lives.

Akshata Shanmugam
NIIT University

I hope this article brings new inspiration to the field of traffic congestion studies and computer vision-based vehicle and violation detection, advancing research towards practical applications in the future and ultimately improving road safety and traffic flow.

dikshant sharma
NIIT University

This paper addresses the need for efficient traffic management in rapidly growing urban areas, where traditional systems struggle with congestion, pollution, and health risks. By exploring traffic control systems that utilize deep learning models like YOLO, we aim to monitor traffic in real-time and dynamically adjust signals to reduce congestion and improve flow. The goal is to contribute to smarter, more adaptive traffic management for the future of urban cities.

Celestin Fernandes
NIIT University

Read the Original

This page is a summary of: Integrated Traffic Management System: A review, August 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3675888.3676111.
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