Dynamic Traffic Optimization through Cloud-Enabled Big Data Analytics and Machine Learning for Enhanced Urban Mobility

Urban mobility is increasingly challenged by traffic congestion, leading to inefficiencies, environmental harm, and economic losses. This research presents an intelligent traffic control system that leverages cloud computing, big data analytics, and machine learning to optimize traffic flow and enhance urban mobility. By collecting and analyzing real-time traffic data from diverse sources, such as sensors, GPS devices, and social media, the system applies RandomForest algorithms for predictive modeling. The system offers dynamic traffic control strategies, including adaptive signal timings and route optimization, which respond to changing conditions in real time. The use of a cloud-based infrastructure ensures scalability and efficient data management. A user-friendly interface facilitates real-time inputs and monitoring, while vehicle simulation aids in predictive analysis and testing. This integrated approach promises to reduce congestion, minimize travel times, and improve overall safety, providing a scalable and flexible solution for urban traffic management.