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. 2022 Sep:110:103428.
doi: 10.1016/j.trd.2022.103428. Epub 2022 Aug 12.

Neighborhood, built environment and resilience in transportation during the COVID-19 pandemic

Weiye Xiao  1 , Yehua Dennis Wei  2 , Yangyi Wu  3
Affiliations

Neighborhood, built environment and resilience in transportation during the COVID-19 pandemic

Weiye Xiao et al. Transp Res D Transp Environ. 2022 Sep.

Abstract

COVID-19 has swept the world, and the unprecedented decline in transit ridership has been noticed. However, little attention has been paid to the resilience of the transportation system, particularly in medium-sized cities. Drawing upon a light rail ridership dataset in Salt Lake County from 2017 to 2021, we develop a novel method to measure the vulnerability and resilience of transit ridership using a Bayesian structure time series model. The results show that government policies have a more significant impact than the number of COVID-19 cases on transit ridership. Regarding the built environment, a highly compact urban design might reduce the building coverage ratio and makes transit stations more vulnerable and less resilient. Furthermore, the high rate of minorities is the primary reason for the drops in transit ridership. The findings are valuable for understanding the vulnerability and resilience of transit ridership to pandemics for better coping strategies in the future.

Keywords: Bayesian structure time series; Build environment; COVID-19; Regression tree; Transit ridership.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
TRAX ridership and COVID-19 exposure in Salt Lake County, Utah.
Fig. 2
Fig. 2
The ridership of the public transit stations, 1/1/2017–8/1/2021.
Fig. 3
Fig. 3
The relative impact of COVID-19 on ridership based on BSTS model and COVID-19 cases, 1/1/2017–8/1/2021.
Fig. 4
Fig. 4
Short-term relative impact based on BSTS model of all the stations in Salt Lake County.
Fig. 5
Fig. 5
Long-term relative impact based on BSTS model of all the stations in Salt Lake County.
Fig. 6
Fig. 6
Resilience index based on BSTS model of all the stations in Salt Lake County.
Fig. 7
Fig. 7
The regression tree model for short-term relative impacts.
Fig. 8
Fig. 8
The regression tree model for long-term relative impacts.
Fig. 9
Fig. 9
The regression tree model for resilience.
Fig. 10
Fig. 10
Resilience and vulnerability of the TRAX stations. Note: we provide the quartile for the variables. First quartile: Low; Second quartile: Medium; Third quartile: High; Fourth quartile: Ex-High.
See this image and copyright information in PMC

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