A discrete-event public transportation simulation model to evaluate travel demand management impacts on waiting times and crowding conditions

Abstract

Several approaches have been proposed and adopted by researchers and decision-makers to improve and deal with public transport operation issues, especially travel demand management (TDM) measures. Disruptions like lockdowns provoked by weather conditions, political riots, special events, natural disaster issues, or the recent COVID-19 pandemic create a need for tools to manage public transport demand and supply o keep users circulating in an efficient, convenient and safe manner. Our work develops a simulation tool of the operations of a public transport system using smart card, GTFS and census data to evaluate the impacts of different intervention scenarios using the pandemic context as a case study. Using a pre-pandemic baseline scenario, we study the impact of several travel demand and public transport supply measures, focusing the analysis on waiting times and crowding conditions inside vehicles and platforms. As a result, we generate easy-to-analyze visual outputs that facilitate prioritizing actions at the metropolitan and district level, identifying where and when waiting times and crowding conditions would exceed certain thresholds.