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. 2023 Jun:137:104307.
doi: 10.1016/j.cities.2023.104307. Epub 2023 Mar 27.

Metro travel and perceived COVID-19 infection risks: A case study of Hong Kong

Mingzhi Zhou  1 , Hanxi Ma  1 , Jiangyue Wu  1 , Jiangping Zhou  1
Affiliations

Metro travel and perceived COVID-19 infection risks: A case study of Hong Kong

Mingzhi Zhou et al. Cities. 2023 Jun.

Abstract

The COVID-19 pandemic has exerted unprecedented impacts on travel behaviors because of people's increased health precautions and the presence of various COVID-19 containment measures. However, little research has explored whether and how people changed their travel with respect to their perceived local infection risks across space and time. In this article, we relate elasticity and resilience thinking to the changes in metro travel and perceived infection risks at the station or community level over time. Using empirical data from Hong Kong, we measure a metro station's elasticity as the ratio of changes in its average trip length to the COVID-19 cases' footprints around that station. We regard those footprints as a proxy for people's perceived infection risks when making trips to that station. To explore influencing factors on travel in the ups and downs of perceived infection risks, we classify stations based on their elasticity values and examine the association between stations' elasticities and characteristics of stations and their served communities. The findings show that stations varied in elasticity values across space and different surges of the local pandemic. The elasticity of stations can be predicted by socio-demographics and physical attributes of station areas. Stations serving a larger percentage of population with higher education degrees and certain occupations observed more pronounced trip length decrease for the same level of perceived infection risks. The number of parking spaces and retail facilities significantly explained variations in stations' elasticity. The results provide references on crisis management and resilience improvement amid and post COVID-19.

Keywords: COVID-19; Elasticity; Perceived infection risks; Resilience; Travel behaviors.

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Conflict of interest statement

The authors have no competing interests to declare.

Figures

Fig. 1
Fig. 1
The research framework.
Fig. 2
Fig. 2
Four local case surges of year 2020 and the weeks selected for elasticity measurement.
Fig. 3
Fig. 3
Kernel Density Estimate (KDE) of station elasticity in four surges.
Fig. 4
Fig. 4
The classified metro stations in four surges.
Fig. 5
Fig. 5
The time-series clustering for station classification
See this image and copyright information in PMC

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