Smart Traffic Systems: A Comprehensive Overview

Smart Traffic Systems: A Comprehensive Overview

Noyal Reji1, Philips Jose2, Sanjayramprasad V S3, Aju Joseph4, Alpha Mathew5

dept. of Computer Science Engineering,

College of Engineering, Kidangoor, Kerala, India

noyalreji100@gmail.com1, philipsjose07@gmail.com2, sanjayramprasadvs@gmail.com3,

ajujoseph007@gmail.com4, alphamathew@ce-kgr.org5

Abstract—Urban traffic congestion represents a critical chal-lenge for modern cities, significantly impacting mobility, envi-ronmental sustainability, and quality of urban life. This com-prehensive survey explores the transformative potential of ad-vanced technologies—particularly deep reinforcement learning (DRL), artificial intelligence (AI), and Vehicle-to-Everything (V2X) communication—in revolutionizing traffic management systems. We critically examine emerging approaches that move beyond traditional fixed-timing signals, highlighting innovative methodologies including deep reinforcement models , computer vision techniques like YOLOv4, and intelligent traffic coordina-tion strategies. The survey synthesizes current research trends, evaluating technological capabilities in real-time traffic opti-mization, emergency vehicle prioritization, and predictive traffic management. By analyzing both technological achievements and existing limitations, we provide a strategic roadmap for future research, emphasizing the need for more adaptive, data-driven, and intelligent urban traffic solutions that can effectively address the complex dynamics of modern transportation networks.

Index Terms—Smart Traffic Management, Deep Reinforce-ment Learning, Artificial Intelligence, Computer Vision, Vehicle-to-Everything Communication, Urban Mobility, Intelligent Transportation Systems