Real-time bus arrival time updating using speed variations

Abstract

Public transportation systems play a critical role in urban mobility, but their efficiency is often hampered by unpredictable delays due to traffic congestion, weather conditions, and other external factors. Accurate real-time bus arrival time predictions are essential to improve the passenger experience, optimize transit operations, and reduce waiting times. Traditional schedule-based estimation methods rely on static timetables, which making them ineffective in dynamic urban environments. This research introduces a data-driven approach of predicting bus arrival in real time using historical and real-time speed variations. By analyzing GPS-based trajectory data and incorporating temporal, spatial, and weather-related characteristics, we aim to improve the accuracy of arrival time estimations.