A double jeopardy: COVID-19 impacts on the travel behavior and community living of people with disabilities

Abstract

The COVID-19 pandemic response has had a significant impact on the general population’s ability to participate in their communities. Individuals with disabilities, an already socially disadvantaged population, are more vulnerable to and have likely been disproportionately impacted by COVID-19 response conditions. Yet, the extent to which daily community living activities of people with disabilities have been impacted is unknown. Thus, this study assesses their travel behavior and community living during the COVID-19 pandemic conditions compared with those of the general population during the same period. A web survey was conducted using Qualtrics’s online panel data (respondents included 232 people with disabilities and 161 people without disabilities). Regression models found that people with disabilities reduced their daily travel to a greater extent but at varying degrees, depending on the destination types and travel modes. Reductions in taxi rides (including ride-hailing services) were most significant among people with cognitive and sensory (vision and hearing) disabilities. By place type, cognitive disability was associated with a trip reduction for multiple destination types—grocery, restaurants, outdoor recreation, indoor recreation, and healthcare providers. Findings from this study could contribute to decision- and policy-making in planning, transportation, and community services during the remainder of the COVID-19 pandemic, in future major public health crises, as well as post-COVID, because the adjustments in travel behavior and community living might be longer-term.

Social and spatial heterogeneities in COVID-19 impacts on individual's metro use: A big-data driven causality inference

“…At the individual-level, Astroza et al (2020) used questionnaire data from a sample of 4395 adults in Chile, and found that females and the elderly are more likely to work from home and thus have lower public transit use like metro use. Park et al (2022) employed survey data and reported that individuals with disabilities were particularly vulnerable during the pandemic as they relied more on public transit and lacked access to alternative transportation modes. Park and Cho (2021) used individual-level smart card data in Korea, which included both senior citizen card and ordinary card, and found that the elderly's fear of COVID-19 was the strongest because of the high fatality rate among the elderly, resulting in the most prominent reduction in metro use among the elderly aged 65 years and over.…”

“…An increasing number of empirical studies have substantiated the change of city-level or station-level metro transit ridership due to the pandemic and mobility policies ( Chang et al, 2021 ; Jiang & Cai, 2022 ; Kwon et al, 2022 ). However, only a limited number of studies have investigated the change of individual-level metro use behaviors, including metro trip frequency, travel time, and mode shift from metro to private cars ( Astroza et al, 2020 ; Park & Cho, 2021 ; Park et al, 2022 ; Maljaee & Sameni, 2022 ). The individual-level research, which is needed more, can help policymakers identify vulnerable socioeconomic groups and disadvantaged groups in metro use during and post the pandemic, promoting effective and equitable mobility intervention policies ( Park et al, 2022 ).…”

“…However, only a limited number of studies have investigated the change of individual-level metro use behaviors, including metro trip frequency, travel time, and mode shift from metro to private cars ( Astroza et al, 2020 ; Park & Cho, 2021 ; Park et al, 2022 ; Maljaee & Sameni, 2022 ). The individual-level research, which is needed more, can help policymakers identify vulnerable socioeconomic groups and disadvantaged groups in metro use during and post the pandemic, promoting effective and equitable mobility intervention policies ( Park et al, 2022 ). Moreover, studies have examined how the effects of mobility interventions vary across different geographical contexts at the city-level, neighborhood-level, and station-level.…”

“…Furthermore, most existing individual-level studies heavily rely on descriptive statistics or correlation analyses to measure the associations between the outbreak and metro use ( Lee, 2022 ; Park & Cho, 2021 ; Park et al, 2022 ), whereas none of these studies explore causal effects. Without a causal inference, the detected associations might be biased, as they could not disentangle the mixed effects of panic effects from virus, policy effects from interventions, and other confounders from weather and holidays ( Hu & Chen, 2021 ).…”

“…As for the ITS design in this study, the MPS big data outperforms traditional small data from questionnaire surveys and self-reported survey combined with mobile phone GPS data ( Parker et al, 2021 ), because of the cost, quality and privacy issues of using questionnaire surveys to trace above 15,000 metro users for three months. Compared to other big datasets such as the commonly used smart card data ( Mützel & Scheiner, 2022 ; Park and Cho, 2022 ; Zhou et al, 2021 ), the MPS data is more suitable for individual-level analysis, particularly when MPS collects rich information of phone users’ mobility trajectories other than merely metro use, and their socioeconomic attributes.…”