Citation

Xi H, Nelson JD, Hensher DA, Hu S, Shao X, Xie C. Transp. Res. A Policy Pract. 2024; 180: e103980.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.tra.2024.103980

PMID

unavailable

Abstract

The COVID-19 pandemic has severely disrupted travel behavior across diverse socio-economic areas, with a significant impact on transportation systems, public health, and the economy. As countries both recover and plan for future virus-driven stresses, it is crucial to identify the drivers of building travel behavior resilience, such as vaccination. Using an integrated dataset with over 150 million US county-level mobile device data from 01/01/2020 to 20/04/2021, we employ Bayesian structural time series (BSTS) models to infer the relative impact of the vaccination intervention on five types of travel behavior across Metropolitan, Micropolitan and Rural areas. Further, we develop partial least squares regression (PLSR) models to accurately estimate how COVID-19 vaccination rates, epidemiological indicators (i.e., COVID-19 incidence rates, death rates, and testing rates) and weather conditions (i.e., temperature, rain, and snow) would impact various travel behaviors across the diverse areas during the recovery period of the pandemic. The model results shed light on the positive role of vaccinations in fostering the recovery of travel behaviors and reveal the disparities in travel behavior resilience in response to vaccination rates, epidemiological indicators, and weather conditions across diverse areas. Our findings can offer evidential insights for policymakers, transport planners, and public health officials, guiding the development of equitable, sustainable, and resilient transportation systems prepared to adapt to future pandemics.

Keywords: CoViD-19-Road-Traffic .

Language: en

Keywords

Bayesian structural time series (BSTS); COVID-19; COVID-19 epidemiological indicators; Partial least squares regression (PLSR); Travel behavior resilience; Vaccination