Smart Traffic Management System: An Ai-powered Iot Framework For Urban Mobility Optimization

Abstract

Urban Traffic Congestion Imposes Significant Economic, Environmental, And Social Burdens On Modern Cities. Traditional Fixed-timing Traffic Control Systems Lack The Adaptability To Handle Dynamic Traffic Flows, Resulting In Inefficiencies, Elevated Emissions, And Delayed Emergency Response. This Paper Presents The Design And Implementation Of A Smart Traffic Management System (STMS) That Integrates Internet Of Things (IoT) Sensor Networks, Artificial Intelligence (AI), And Machine Learning (ML) Algorithms To Enable Real-time, Adaptive Traffic Control. The Proposed System Employs A Distributed Sensor Architecture To Collect Vehicle Density, Speed, And Weather Data, Which Is Processed By A Cloud-based AI Engine Employing Random Forest Regression And Reinforcement Learning For Signal Optimization And Congestion Prediction. Vehicle-to-Infrastructure (V2I) Communication Relays Updates To Connected Vehicles. Algorithms Including Dijkstra's Shortest Path, A* Search, Genetic Algorithm, And K-Means Clustering Underpin Route Planning And Traffic Pattern Analysis. Simulation-based Evaluation Demonstrates Significant Reductions In Average Vehicle Wait Times And Improved Intersection Throughput Compared To Conventional Systems. The STMS Also Incorporates An Incident Detection Module Capable Of Rapid Anomaly Identification And Emergency Rerouting. Results Confirm The Viability Of This Integrated Approach For Scalable, Sustainable Urban Mobility Management.

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