Who would continue to work from home in Hong Kong as the COVID-19 pandemic progresses?

This study aims a more thorough understanding of individuals' motivations and determinants of working from home (WFH) at various phases of the pandemic. To achieve this research goal, we analyze attitudes towards WFH, the profiles of various types of workers engaged in WFH, and the determinants of the current and future expected frequency of WFH among 816 workers in Hong Kong. We identify four types of teleworkers: (1) those with little employer support, (2) those distracted with tech problems, (3) those with good home office, and (4) those with substantial employer support. Separate latent-class choice models present that WFH frequencies in early phases of the pandemic (and at the moment), attitudes towards WFH, and certain constraining/facilitating factors affect the (expected) frequency of WFH. This study provides valuable insights into the types of teleworkers and the determinants of WFH, which will help policymakers create ways to encourage (or discourage) the future frequency of WFH.

Longitudinal Analysis of COVID-19 Impacts on Mobility: An Early Snapshot of the Emerging Changes in Travel Behavior

The COVID-19 pandemic has caused a huge disruption worldwide with direct and indirect effects on travel behavior. In response to extensive community spread and potential risk of infection, during the early stage of the pandemic many state and local governments implemented non-pharmaceutical interventions that restricted non-essential travel for residents. This study evaluates the impacts of the pandemic on mobility by analyzing micro panel data (N = 1,274) collected in the United States via online surveys in two periods, before and during the early phase of the pandemic. The panel makes it possible to observe initial trends in travel behavior change, adoption of online shopping, active travel, and use of shared mobility services. This analysis intends to document a high-level overview of the initial impacts to spur future research to dive deeper into these topics. With the analysis of the panel data, substantial shifts are found from physical commutes to teleworking, more adoption of e-shopping and home delivery services, more frequent trips by walking and biking for leisure purposes, and changes in ridehailing use with substantial variations across socioeconomic groups. The social and environmental implications of these findings are discussed and suggestions for effective policy and directions for future research are made in the conclusion.

Longitudinal Analysis of COVID-19 Impacts on Mobility: An Early Snapshot of the Emerging Changes in Travel Behavior.

The COVID-19 pandemic has caused a huge disruption worldwide with direct and indirect effects on travel behavior. In response to extensive community spread and potential risk of infection, during the early stage of the pandemic many state and local governments implemented non-pharmaceutical interventions that restricted non-essential travel for residents. This study evaluates the impacts of the pandemic on mobility by analyzing micro panel data (N = 1,274) collected in the United States via online surveys in two periods, before and during the early phase of the pandemic. The panel makes it possible to observe initial trends in travel behavior change, adoption of online shopping, active travel, and use of shared mobility services. This analysis intends to document a high-level overview of the initial impacts to spur future research to dive deeper into these topics. With the analysis of the panel data, substantial shifts are found from physical commutes to teleworking, more adoption of e-shopping and home delivery services, more frequent trips by walking and biking for leisure purposes, and changes in ridehailing use with substantial variations across socioeconomic groups. The social and environmental implications of these findings are discussed and suggestions for effective policy and directions for future research are made in the conclusion.

Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents.

Introduction: This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment. Methods: Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities. Results: Results of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city. Discussion: Integrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.

Substitution or complementarity? A latent-class cluster analysis of ridehailing impacts on the use of other travel modes in three southern U.S. cities

In this study, we explore the heterogeneous impacts of ridehailing on the use of other travel modes using survey data (N = 1,438) collected from June to October 2019 (i.e., before the COVID-19 pandemic) across three regions in southern U.S. states: Phoenix, Arizona;Atlanta, Georgia;and Austin, Texas. We apply a latent-class cluster analysis to indicators of changes in the use of various travel modes as a result of ridehailing adoption, with covariates of socioeconomics, demographics, a land-use attribute, and individual attitudes. We identify four distinctive latent classes of behavioral changes in response to the use of ridehailing. About half of ridehailing users in the sample (49.7%) are found to behave as Mobility augmenters, who use ridehailing rarely, in addition to other travel modes, and do not change their travel routines much as a result of the adoption of this mobility service. The second largest class includes Exogenous changers (24.5%), whose members report many changes in their use of various travel modes, but which can be largely explained by other reasons. Private car/taxi substituters (15%) frequently hail a ride, and as a result, reduce their use of private vehicles while making more trips by public transit and active modes, as the result of using ridehailing. Interestingly, Transit/active mode substituters (10.8%) often use ridehailing, likely for trips that they previously made by public transit or active modes, and consequently reduce their use of these less-polluting modes while enjoying enhanced mobility. This study reveals substantial heterogeneity in ridehailing impacts, which were masked in previous studies that focused on average impacts, and it suggests that policy responses should be customized by users’ socioeconomics and residential neighborhoods.

Longitudinal Analysis of COVID-19 Impacts on Mobility: An Early Snapshot of the Emerging Changes in Travel Behavior

The COVID-19 pandemic has caused huge disruption to society with, among other impacts, direct and indirect effects (e.g. through public health measures) on travel behavior. Since its initial outbreak, COVID-19 has manifested itself into a global pandemic. In response to extensive community spread and potential risk of infection, many state and local governments implemented stay-at-home orders along with measures for social distancing restricting non-essential travel for residents. These travel advisories imposed broad restrictions on millions of Americans resulting in drastic changes in mobility and disruptions to economic activity. In this study the authors use a combination of data from two previous online surveys and a current data collection conducted to evaluate the impacts of the pandemic on mobility to form a unique longitudinal panel. The use of a longitudinal panel provides us the ability to observe initial trends in travel behavior change, adoption of online shopping, active travel and use of shared mobility services. In the analysis present initial descriptive statistics from the sample to examine the changes in various components of travel behavior in the sample (N=1,274) and for each income/occupation group separately. The authors find substantial shifts from physical commutes to teleworking, more adoption of e-shopping and home delivery services, more frequent trips by walking and biking for leisure purposes, and changes in ride-hailing use. Also, the authors discuss implications of these findings from the perspectives of environmental sustainability and social equity. This study concludes with suggestions of directions for effective policy and future research.