Public Transport Passenger Attraction Using Policy Interventions for a Post-COVID Scenario

Abstract

Public transport (PT) ridership has seen a continual decline over the past years as more people are opting to use private vehicles, contributing to increased traffic congestions, road accidents, pollution, etc. The COVID-19 pandemic has exacerbated this problem, with official guidelines discouraging the use of PT systems to prevent contagion. Passenger attraction policies for a post-pandemic phase should be formulated by examining the changes in daily trips brought about by the COVID-induced lockdowns. With data collected from employees working in Thiruvananthapuram city, the present study develops a post-lockdown mode-choice model using fuzzy logic programming to evaluate different policies to increase PT ridership. The policies such as introducing parking prohibition on major streets, reducing return-trip fares, improving PT coverage and supply, and early-bird pre-peak hour discounts were tested using sensitivity analysis and the choice model estimated a (private to PT) modal shift of 5.8%, 6.9%, 6.2% and 6.2%, respectively. It is concluded that passenger attraction policies should concentrate more on improving PT services than discouraging private modes to improve ridership.