Work

AI based Traffic Management System

Year: 2025

Type: article

Abstract: Urban traffic congestion is an escalating problem driven by increasing vehicle numbers and outdatedtraffic management systems. Traditional methods lack flexibility, leading to prolonged delays, increasedfuel consumption, and higher pollution levels. This paper introduces an AI-driven adaptive trafficmanagement system leveraging real-time data, machine learning, and predictive analytics to optimize trafficflow. The proposed system uses real-time traffic monitoring and dynamic signal control to reducecongestion, minimize emissions, and improve road efficiency, contributing to a sustainable transportationecosystem.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16788707

Author: Tanuja Jagtap, Meghana Shivaprasad, Harshad Potdar, Dheeraj Burli, Prof. Sushant Shirbhate

Language: English

Topic: Traffic control and management

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: green

OpenAlex ID: W6968437293

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