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Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks

INTRODUCTION: Unlike previous pandemics, COVID-19 has sustained over a relatively longer period with cyclical infection waves and numerous variants. Public transport ridership has been hit particularly hard. To restore travellers’ confidence it is critical to assess their risk determinants and trade...

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Autores principales: Shelat, Sanmay, van de Wiel, Thijs, Molin, Eric, van Lint, J. W. C., Cats, Oded
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8893614/
https://www.ncbi.nlm.nih.gov/pubmed/35239712
http://dx.doi.org/10.1371/journal.pone.0264805
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author Shelat, Sanmay
van de Wiel, Thijs
Molin, Eric
van Lint, J. W. C.
Cats, Oded
author_facet Shelat, Sanmay
van de Wiel, Thijs
Molin, Eric
van Lint, J. W. C.
Cats, Oded
author_sort Shelat, Sanmay
collection PubMed
description INTRODUCTION: Unlike previous pandemics, COVID-19 has sustained over a relatively longer period with cyclical infection waves and numerous variants. Public transport ridership has been hit particularly hard. To restore travellers’ confidence it is critical to assess their risk determinants and trade-offs. METHODS: To this end, we survey train travellers in the Netherlands in order to: (i) quantify the impact of trip-specific, policy-based, and pandemic-related attributes on travellers’ COVID-19 risk perceptions; and (ii) evaluate the trade-off between this risk perception and other travel attributes. Adopting the hierarchical information integration approach, in a two-stage stated preference experiment, respondents are asked to first rate how risky they perceive different travel situations to be, and then to choose between different travel options that include their own perceived risk rating as an attribute. Perceived risk ratings and choices between travel options are modelled using a linear regression and a mixed multinomial logit model, respectively. RESULTS: We find that on-board crowding and infection rates are the most important factors for risk perception. Amongst personal characteristics, the vulnerability of family and friends has the largest impact—nearly twice that of personal health risk. The bridging choice experiment reveals that while values of time have remained similar to pre-pandemic estimates, travellers are significantly more likely to choose routes with less COVID-19 risk (e.g., due to lower crowding). Respondents making longer trips by train value risk four times as much as their shorter trip counterparts. By combining the two models, we also report willingness to pay for mitigating factors: reduced crowding, mask mandates, and increased sanitization. CONCLUSION: Since we evaluate the impact of a large number of variables on route choice behaviour, we can use the estimated models to predict behaviour under detailed pandemic scenarios. Moreover, in addition to highlighting the importance of COVID-19 risk perceptions in public transport route choices, the results from this study provide valuable information regarding the mitigating impacts of various policies on perceived risk.
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spelling pubmed-88936142022-03-04 Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks Shelat, Sanmay van de Wiel, Thijs Molin, Eric van Lint, J. W. C. Cats, Oded PLoS One Research Article INTRODUCTION: Unlike previous pandemics, COVID-19 has sustained over a relatively longer period with cyclical infection waves and numerous variants. Public transport ridership has been hit particularly hard. To restore travellers’ confidence it is critical to assess their risk determinants and trade-offs. METHODS: To this end, we survey train travellers in the Netherlands in order to: (i) quantify the impact of trip-specific, policy-based, and pandemic-related attributes on travellers’ COVID-19 risk perceptions; and (ii) evaluate the trade-off between this risk perception and other travel attributes. Adopting the hierarchical information integration approach, in a two-stage stated preference experiment, respondents are asked to first rate how risky they perceive different travel situations to be, and then to choose between different travel options that include their own perceived risk rating as an attribute. Perceived risk ratings and choices between travel options are modelled using a linear regression and a mixed multinomial logit model, respectively. RESULTS: We find that on-board crowding and infection rates are the most important factors for risk perception. Amongst personal characteristics, the vulnerability of family and friends has the largest impact—nearly twice that of personal health risk. The bridging choice experiment reveals that while values of time have remained similar to pre-pandemic estimates, travellers are significantly more likely to choose routes with less COVID-19 risk (e.g., due to lower crowding). Respondents making longer trips by train value risk four times as much as their shorter trip counterparts. By combining the two models, we also report willingness to pay for mitigating factors: reduced crowding, mask mandates, and increased sanitization. CONCLUSION: Since we evaluate the impact of a large number of variables on route choice behaviour, we can use the estimated models to predict behaviour under detailed pandemic scenarios. Moreover, in addition to highlighting the importance of COVID-19 risk perceptions in public transport route choices, the results from this study provide valuable information regarding the mitigating impacts of various policies on perceived risk. Public Library of Science 2022-03-03 /pmc/articles/PMC8893614/ /pubmed/35239712 http://dx.doi.org/10.1371/journal.pone.0264805 Text en © 2022 Shelat et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Shelat, Sanmay
van de Wiel, Thijs
Molin, Eric
van Lint, J. W. C.
Cats, Oded
Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks
title Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks
title_full Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks
title_fullStr Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks
title_full_unstemmed Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks
title_short Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks
title_sort analysing the impact of covid-19 risk perceptions on route choice behaviour in train networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8893614/
https://www.ncbi.nlm.nih.gov/pubmed/35239712
http://dx.doi.org/10.1371/journal.pone.0264805
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