Performance Evaluation of Public Transport Networks and Its Optimal Strategies Under Uncertainty

Abstract

The study introduces a novel framework to enhance public transportation performance in uncertain situations, incorporating multi-aspiration-level goal programming and Monte Carlo simulation to manage uncertainty. The process involves creating a public transport criteria matrix using an analytic hierarchy process and optimizing the network based on weight results. Three Australian case studies are used to validate the proposed methodology.