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

Abstract

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