Traffic Management Systems: A Comprehensive Literature Review

Tanish Shah

Abstract: The traffic systems used are fixed timing signals, which are programmed and can't adjust to changes in traffic, and loop inductive systems, which have been criticized for their limited foresight. Inefficient, traditional traffic management systems lead to traffic congestion, which further leads to economic losses, fuel wastage, and worsens air quality. This review looks at intelligent traffic management solutions through the integration of Artificial Intelligence and the role of IoT in better deployment of AI in urban settings. The review also particularly looks at how IoT systems through a variety of sensors and other infrastructure. This review also looks at an AI model-SURTRAC- that shows real world potential and reduction in waiting times. This review calls for the need for AI implementation into traffic systems and tries to point out research gaps that need to be addressed.

Keywords: Traffic Management Systems, Traffic Control, Fixed-Timing Systems, Inductive-Loop Systems, Artificial Intelligence (AI), Traffic Prediction, Traffic Optimization, Reinforcement Learning, Computer Vision, IoT-Based Systems, Traffic Monitoring, SURTRAC, Traffic Congestion, Emissions, Urban Mobility, Sustainability

How to Cite?: Tanish Shah, "Traffic Management Systems: A Comprehensive Literature Review", Volume 14 Issue 7, July 2025, International Journal of Science and Research (IJSR), Pages: 1362-1366, https://www.ijsr.net/getabstract.php?paperid=SR25717132058, DOI: https://dx.doi.org/10.21275/SR25717132058