ABSTRACT
Teleworking (i.e., working from home), with the aid of teleworking technologies, became widespread over the world as an impact of COVID-19. The long-term impact of teleworking in the future on commuting and social equity is discussed by the experts. However, less attention has been paid to the factors that are associated with people's choice to start teleworking for the first time or existing teleworkers' choice to increase the current frequency. This study investigates the changes in preference for teleworking frequency in the post-pandemic era. From a survey of 301 respondents in New York City, respondents are split into three categories. These are (1) previous teleworkers who do not want to increase their teleworking frequency, (2) previous teleworker who want to increase their frequency (i.e., extended teleworker), and (3) previous non-teleworker who wants to start teleworking (i.e., prospective teleworker) as the city reopens. A multinomial logit model is used to predict these categories with the help of several sociodemographic, household, geographic, travel behavioral, and attitudinal characteristics of the respondents. The model suggests that younger people and non-Hispanic people are more likely to extend or start teleworking than their counterparts. Females, Blacks, low-income people, and people with a child under five years are more likely to start teleworking while their counterparts (i.e., males, non-Blacks, high-income people, and people with a child under five) are more inclined towards extending teleworking. More work-trip makers and public transit users (for grocery) have less probability to extend teleworking. People with more pro-street and pro-out-migration attitudes and less pro-safety attitudes are more interested in starting or extending teleworking. The findings help targeted investment for post-pandemic accessibility, travel demand management, and energy efficiency.
ABSTRACT
INTRODUCTION: Human mobility has been a central issue in the discussion from the beginning of COVID-19. While the body of literature on the relationship of COVID transmission and mobility is large, studies mostly captured a relatively short timeframe. Moreover, spatial non-stationarity has garnered less attention in these explorative models. Therefore, the major concern of this study is to see the relationship of mobility and COVID on a broader temporal scale and after mitigating this methodological gap. OBJECTIVE: In response to this concern, this study first explores the spatiotemporal pattern of mobility indicators. Secondly, it attempts to understand how mobility is related to COVID infection rate and how this relationship has been changed over time and space after controlling several sociodemographic characteristics, spatial heterogeneity, and policy-related changes during different phases of Coronavirus. DATA AND METHOD: This study uses GPS-based mobility data for a wider time frame of six months (March 20-August'20) divided into four tiers and carries analysis for all the US counties (N = 3142). Space-time cube is used to generate the spatiotemporal pattern. For the second objective, Ordinary Least Square (OLS), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR) were used. RESULT: The spatial-temporal pattern suggests that the trip rate, out-of-county trip rate, and miles/person traveled were mostly plummeted till the first wave reached its peak, and subsequently, all of these mobility matrices started to rise. From spatial models, infection rates were found negatively correlated with miles traveled and out-of-county trips. Highly COVID infected areas mostly had more people working from home, low percentages of aged people and educated people, and high percentages of poor people. CONCLUSION: This study, with necessary policy implications, provides a comprehensive understanding of the shifting pattern of mobility and COVID. Spatial models outperform OLS with better fits and non-clustered residuals.