Urban Mobility Optimization Using IoT Data Fusion and XGBoost for Shared Transportation

In modern cities, optimizing transport networks is crucial to reducing congestion, improving efficiency, and promoting sustainability. An advanced urban mobility optimization framework using Internet of Things (IoT) technologies and the XGBoost algorithm is presented in the research. The platform fuses IoT-driven data streams from automobiles, bikes, scooters, traffic infrastructure, and environmental sensors. Real-time car positions, traffic flow patterns, parking availability, environmental conditions, and more are available from these data sources. The XGBoost algorithm, an advanced machine learning method that handles complicated data interactions and performs well predictively, makes the framework operate. After careful training on the merged dataset, the XGBoost model may identify complex patterns and provide meaningful insights into traffic dynamics, demand forecasts, route optimization, and parking management. The synergy of IoT data fusion with the XGBoost algorithm helps optimize urban transportation. Transport authorities may reduce traffic congestion, optimize shared transport services, and boost urban mobility network efficiency using real-time IoT sensor data. The XGBoost model's predictive capabilities allow anticipatory decision-making, maximizing resource efficiency and effectiveness.