Smart Urban Mobility: Integrating IoT and Big Data Analytics for Traffic Optimization

Abstract

Urban traffic congestion remains a critical challenge impacting economic productivity and environmental health. This research develops a smart urban mobility system that integrates IoT sensor networks with big data analytics to optimize traffic flow in real time. The system collects data from vehicular sensors, GPS devices, and traffic cameras, applying predictive analytics and machine learning models to anticipate congestion and adjust traffic signals dynamically. Pilot deployment in a metropolitan area reduced average travel time by 18% and emissions by 12%, demonstrating the effectiveness of convergent technologies in building sustainable smart cities.