Authors : KM. Poonam

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/38wtvcw2

Scribd : https://tinyurl.com/3cc4yucf

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT1444

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Urban traffic management remains one of the most complex challenges of modern cities, with congestion, inefficiencies, and accidents costing billions of dollars annually and contributing significantly to pollution and stress. Current traffic management systems are often reactive rather than predictive, responding to congestion and incidents after they occur. This paper introduces a novel AI-driven predictive analysis framework for urban traffic management that leverages advanced machine learning (ML) algorithms and real-time data inputs. The system aims to not only manage existing traffic efficiently but to predict congestion, optimize traffic flows, and enhance computer safety proactively. We explore the integration of multiple data sources—such as GPS data, traffic cameras, IoT sensors, and social media feeds—into a cohesive AI model that learns and evolves. The goal is to create a fully autonomous traffic management system that adjusts dynamically to urban changes, improving overall city mobility, sustainability, and quality of life.

Keywords : AI, Predictive Analysis, Urban Traffic Management, Machine Learning, Smart Cities, Traffic Forecasting.

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