Paper Key : IRJ************952
Author: Dr.s. P. Sonar,Aishwarya A Dabhade,Vaishnavi P Dhamale,Akshada B Kurhade
Date Published: 06 Apr 2025
Abstract
This project introduces an Intelligent Traffic Management and Vehicle Detection System designed to optimizeurban mobility using advanced artificial intelligence techniques. Leveraging state-of-the-art machine learningmodels, particularly Convolutional Neural Networks (CNNs), the system processes live video streams fromstrategically positioned cameras to detect and classify vehicles in real time. This enables precise vehicle counting,traffic density measurement, and the identification of congestion trends.The system architecture integrates a combination of hardware, such as IP cameras and edge computing units,with powerful software tools like TensorFlow and OpenCV. A user-friendly dashboard is developed to providedynamic traffic monitoring and data visualization, allowing urban planners and traffic authorities to gain realtime insights into traffic conditions and make informed decisions on managing congestion.This system demonstrates a marked improvement in traffic flow monitoring, offering actionable insights thatcontribute to more efficient urban infrastructure development and traffic policy formulation. By incorporatingAI, the solution aims to create smarter, safer cities, enhancing the overall performance of transportationnetworks. Additionally, the system's flexibility allows it to incorporate supplementary data streamssuch asweather information and public transport dataoffering a more comprehensive view of urban mobilitydynamics. This all-encompassing approach not only aids in immediate traffic management but also providesvaluable support for long-term urban planning strategies.