Travel behaviour during unplanned train disruptions

Considering changed behaviour due to the COVID-19 pandemic

Master thesis (2022)

Authors

J.E.B. Bickel Civil Engineering & Geosciences

Contributors

E.J.E. Molin Transport and Logistics - TPM (mentor)
N. van Oort (graduation committee member)
N. Geržinič (graduation committee member)
M. de Bruyn Nederlandse Spoorwegen (coach)
Faculty
Civil Engineering & Geosciences, Civil Engineering & Geosciences
Copyright: © 2022 Julia Bickel
More Info
expand_more

Published Date

06-10-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Civil Engineering & Geosciences

Abstract

The aim of this study is to capture heterogeneity in travel behaviour during unplanned train disruptions focusing on the rise of teleworking due to the COVID-19 pandemic to improve train services and help predict passenger flows during disruptions. To perform the analyses, a dataset is elicited from 815 Dutch train commuters by distributing an online questionnaire. A labelled stated choice experiment was designed and a latent class choice model was estimated. The biggest indicators of travel behaviour are the moment of discovering the disruption, the disruption length and job characteristics. Four latent classes were uncovered: the ’Trade-off teleworkers’, ’Sceptic returners’, ’Trusting workplace travellers’ and ’Endless waiters’. Each class has a different initial preference for a travel option and different sensitivities to the travel attributes as well. Individual
characteristics such as age, necessity to arrive at the workplace, ability to telework, telework attitude and the amount of trust in the provided travel information play a role in predicting choice behaviour during unplanned train disruptions. Based on the results advice is given on how the level of service during disruptions can be improved.

Files

Master_Thesis_JEB_Bickel.pdf
(pdf | 9.79 Mb)
Unknown license