Abstract
In developing nations like India, due to non-homogeneity in traffic streams, the flow of motorized three-wheelers gets clogged. While studying the literature, researchers do not find any significant Level of Service (LOS) models for forecasting motorized three-wheelers' service quality at uncontrolled un-signalized intersections under heterogeneous traffic conditions. This study brings to an AI-based Three-Wheeler Level of Service (3WhLOS) model to evaluate the service quality offered by un-signalized intersections operating under mixed traffic conditions. Data are collected from 21 uncontrolled intersections located at 7 different cities of India. Spearman's correlation analysis is performed to fathom the influence of service parameters towards perceived 3WhLOS score. Bayesian Regularized Artificial Neural Network (BRANN) is adopted for the prediction of 3WhLOS scores. Sensitivity Analysis is also executed to determine the relative importance of each parameter and help the transport authorities to identify the issues and improvise them for the betterment of the users.
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