Machine Learning-Enabled Smart Transit: Real-Time Bus Tracking System for Enhanced Urban Mobility

In bustling urban cities, the efficiency of public transportation stands as a critical factor in alleviating traffic congestion and enhancing the daily commuter experience. This paper introduces a state-of-the-art Bus Tracking System that is meticulously designed to empower passengers by providing real-time updates on bus locations, availability, and estimated arrival and departure times. By leveraging cutting-edge technologies, this innovative system offers a myriad of advantages, notably including the reduction of passenger wait times, the optimization of route planning, and an overall enhancement of the public transportation journey. Through a comprehensive exploration of the significance of bus tracking technology and its potential to revolutionize urban mobility, this research underscores its pivotal role in promoting sustainable, convenient, and seamless transportation solutions tailored to the needs of modern cities. Our model uses machine learning techniques like regression, Kalman filter algorithm, and support vector machines to analyze the accuracy of arrival and departure of buses and further help in time-efficient and demand-based management of buses. A real-time location of buses can be provided by Google Maps API and Google Geolocation API.