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

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