Citation

Xu N, Nie Q, Liu J, Jones S. Transp. Policy 2024; 151: 46-62.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.tranpol.2024.04.002

PMID

unavailable

Abstract

This study examines the impacts of the COVID-19 pandemic on short-term travel behavior and long-term travel preferences among residents of Alabama, using survey data. The study employs a two-step path analysis modeling framework to connect the changes in travel behavior during the pandemic with the expected long-term travel preferences. In the first step, the study develops a model to identify correlates of the travel behavior changes during the pandemic (i.e., reduced trip frequency to grocery/pharmacy, retail/recreation, and work/commuting). In the second step, the study develops a model to establish the association between during-pandemic travel behavior changes and post-pandemic travel preferences. To reduce estimation bias when relying on one single model, the study employs multiple machine learning classifiers such as Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Average marginal effects are estimated to quantify the correlates of travel impacts due to the pandemic. The results reveal that fear of COVID-19 is significantly associated with reduced travel during the pandemic regardless of the trip type, and people from high-income households will likely travel less after the pandemic. The path analysis connects the correlates of short-term travel impacts of COVID-19 and long-term travel preferences, and identifies variables such as fear of COVID-19 that are significantly linked to reduced travel during the pandemic and also associated with long-term travel preferences. People who reduced their travel during the pandemic are likely to continue traveling less in the future. The study provides valuable insights into the impacts of COVID-19 on travel behavior in Alabama, which can inform the development of local transportation policies.

Keywords: CoViD-19-Road-Traffic

Language: en

Keywords

COVID-19 pandemic; Machine learning; Path analysis; Travel behavior