Hybrid travel time estimation model for public transit buses using limited datasets

Abstract

A reliable transit service can motivate commuters to switch their travelingmode from private to public. Providing necessary information to passengerswill reduce the uncertainties encountered during their travel and improveservice reliability. This article addresses the challenge of predicting dynamictravel times in urban areas where real-time traffic flow information isunavailable. In this perspective, a hybrid travel time estimation model(HTTEM) is proposed to predict the dynamic travel time using the predictedtravel times of the machine learning model and the preceding trip details. Theproposed model is validated using the location data of public transit buses of,Tumakuru, India. From the numerical results through error metrics, it is foundthat HTTEM improves the prediction accuracy, finally, it is concluded that theproposed model is suitable for estimating travel time in urban areas withheterogeneous traffic and limited traffic infrastructure.