Paper Key : IRJ************858
Author: Swapnali Bakal,Gauri Kokate,Anuradha Virkar,Sejal Pol
Date Published: 22 Apr 2024
Abstract
Managing the increasing number of vehicles in urban areas presents a persistent challenge. Traffic narrowing disrupts daily routines, elevates stress levels, and contributes to higher carbon emissions, impacting the environment. With the rise in population, megacities need to catch up on transportation activities. An intelligent traffic management system is essential to monitor traffic density continuously and take proactive measures. Although there are designated lanes for different vehicle types, wait times at traffic signals remain unchanged. To address this, we propose leveraging artificial intelligence to gather real-time images from signals and improve the current system.Our methodology uses the YOLO image processing technique to assess traffic density accurately. YOLO is brilliant in detecting multiple vehicles, enhancing the system's effectiveness in managing traffic jams. Intelligent monitoring technology integrates a signal-switching algorithm at intersections to optimize time distribution and mitigate traffic congestion. This approach aims to reduce vehicle waiting times, improving overall traffic flow.
DOI LINK : 10.56726/IRJMETS53192 https://www.doi.org/10.56726/IRJMETS53192