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

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