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Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data

The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measu...

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Autores principales: Xu, Ningzhe, Nie, Qifan, Liu, Jun, Jones, Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040369/
https://www.ncbi.nlm.nih.gov/pubmed/37008746
http://dx.doi.org/10.1016/j.tbs.2023.100584
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author Xu, Ningzhe
Nie, Qifan
Liu, Jun
Jones, Steven
author_facet Xu, Ningzhe
Nie, Qifan
Liu, Jun
Jones, Steven
author_sort Xu, Ningzhe
collection PubMed
description The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people’s post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians’ post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alabamians’ perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30–45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local transportation plans that consider the impacts of the pandemic on future travel intentions.
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spelling pubmed-100403692023-03-27 Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data Xu, Ningzhe Nie, Qifan Liu, Jun Jones, Steven Travel Behav Soc Article The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people’s post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians’ post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alabamians’ perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30–45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local transportation plans that consider the impacts of the pandemic on future travel intentions. Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. 2023-07 2023-03-27 /pmc/articles/PMC10040369/ /pubmed/37008746 http://dx.doi.org/10.1016/j.tbs.2023.100584 Text en © 2023 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Xu, Ningzhe
Nie, Qifan
Liu, Jun
Jones, Steven
Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data
title Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data
title_full Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data
title_fullStr Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data
title_full_unstemmed Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data
title_short Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data
title_sort post-pandemic shared mobility and active travel in alabama: a machine learning analysis of covid-19 survey data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040369/
https://www.ncbi.nlm.nih.gov/pubmed/37008746
http://dx.doi.org/10.1016/j.tbs.2023.100584
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