Smart Hybrid Bus Tracking System with Mobile Sensing and Real-Time Updates

Abstract: Growing urban populations have created an increasing need for intelligent public transportation systems, with real-time bus tracking becoming a critical requirement. Traditional approaches rely extensively on specialized GPS equipment, creating significant costs and operational challenges. This research presents a Smart Hybrid Bus Tracking System that utilizes smartphones that drivers and passengers already carry, removing the requirement for dedicated hardware. The system uses the driver's mobile device as the main location provider, with passenger smartphone data enhancing position precision. During network outages, the platform saves information locally and updates it when connectivity returns. The system also integrates sophisticated algorithms such as Kalman Filter, XGBoost, and LSTM to improve prediction accuracy. This approach delivers a cost-effective, flexible, and practical tracking solution that works effectively across various real-world applications.

Keywords: Bus Tracking, GPS, Mobile Sensing, Real-Time System, Kalman Filter, XGBoost, LSTM, Smart Transportation