How crowding impedance affected travellers on public transport in the COVID-19 pandemic

Shin Hyung Cho, Ho Chul Park, Sangho Choo, Shin Hyoung Park

Research output: Contribution to journalArticlepeer-review

Abstract

In the aftermath of the COVID-19 pandemic, travel behaviour has changed significantly. Governors have introduced different transport policies to maintain the travel demand in the public transport system. Previous studies have developed the measurement of crowding impedance on public transport to determine the degree of transit use and the impact of the COVID-19 pandemic. This study explores the behavioural differences in crowding impedance to provide transport policies incorporating group segmentations. The D-efficient design process has structured a survey with a reasonable choice set, and questionnaires have been provided to identify the attitudinal groups of travellers effectively. The travellers are divided into four groups according to the values of factor loadings from the factor analysis: i.e., fear of disease, transit preference, time sensitivity, and auto preference. Multinomial logit models explore the behavioural differences in route and mode choices and calculate crowding multipliers. The results show that the group with a fear of disease comprises a high proportion of the elderly owing to their reluctance to expose themselves to infectious diseases; furthermore, the time-sensitive group exhibits less crowding impedance on public transport. Thus, the crowding multipliers differ between the groups and influence the relevant transport policies to promote public transport use. Policymakers are encouraged to introduce customized transport policies depending on the requirements of each group of travellers to cope with the adverse effects of the pandemic.

Original languageEnglish
Pages (from-to)69-83
Number of pages15
JournalTransportation Research Part F: Traffic Psychology and Behaviour
Volume100
DOIs
StatePublished - Jan 2024

Keywords

  • COVID-19 pandemic
  • Crowding Impedance
  • Factor Analysis
  • Stated Preference Survey
  • Transport Policy
  • Travel Behaviour

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