ABSTRACT
The COVID-19 pandemic has tremendously affected the whole of human society worldwide. Travel patterns have greatly changed due to the increased risk perception and the governmental interventions regarding COVID-19. This study aimed to identify contributing factors to the changes in public and private transportation mode choice behavior in China after COVID-19 based on an online questionnaire survey. In the survey, travel behaviors in three periods were studied: before the outbreak (before 27 December 2019), the peak (from 20 January to 17 March 2020), and after the peak (from 18 March to the date of the survey). A series of random-parameter bivariate Probit models was developed to quantify the relationship between individual characteristics and the changes in travel mode choice. The key findings indicated that individual sociodemographic characteristics (e.g., gender, age, ownership, occupation, residence) have significant effects on the changes in mode choice behavior. Other key findings included (1) a higher propensity to use a taxi after the peak compared to urban public transportation (i.e., bus and subway);(2) a significant impact of age on the switch from public transit to private car and two-wheelers;(3) more obvious changes in private car and public transportation modes in more developed cities. The findings from this study are expected to be useful for establishing partial and resilient policies and ensuring sustainable mobility and travel equality in the post-pandemic era.
ABSTRACT
COVID-19 has upended the whole world. Due to travel restrictions by governments and increased perceived risks of the disease, there have been significant changes in social activities and travel patterns. This paper investigates the effects of COVID-19 on changes to individuals' travel patterns, particularly for travel purposes. An online questionnaire survey was conducted in China, which incorporates questions about individuals' sociodemographic and travel characteristics in three different periods of COVID-19 (i.e. before the outbreak, at the peak and after the peak;the peak here refers to the peak of the pandemic in China, between the end of January and 1 May, 2020). The results show that trip frequency decreased sharply from the outbreak until the peak, and drastically increased after the peak. Nevertheless, the data from this study suggests that it has not fully recovered to the level before the outbreak. Subsequently, a series of random parameters bivariate Probit models for changes in travel patterns were estimated with personal characteristics. The findings demonstrate that during the peak of the pandemic, residents who did not live in more developed cities reached low-frequency travel patterns more quickly. For travel purposes, residents of Wuhan, China resumed travelling for work, entertainment and buy necessities at a much higher rate than other cities. After the peak, students' travel for work, entertainment and to buy necessities recovered significantly faster than for other occupations. The findings would be helpful for establishing effective policies to control individual travel and minimize disease spread in a possible future pandemic.
ABSTRACT
5 In the early days of the COVID-19 pandemic, an area could only report its first positive cases if the infection had spread into the area and if the infection was subsequently detected. A standard probit model does not correctly account for these two distinct latent processes but assumes there is a single underlying process for an observed outcome. A similar issue confounds research on other binary outcomes such as corporate wrongdoing, acquisitions, hiring, and new venture establishments. The bivariate probit model enables empirical analysis of two distinct latent binary processes that jointly produce a single observed binary outcome. One common challenge of applying the bivariate probit model is that it may not converge, especially with smaller sample sizes. We use Monte Carlo simulations to give guidance on the sample characteristics needed to accurately estimate a bivariate probit model. We then demonstrate the use of the bivariate probit to model infection and detection as two distinct processes behind county-level COVID-19 reports in the United States. Finally, we discuss several organizational outcomes that strategy scholars might analyze using the bivariate probit model in future research.
ABSTRACT
After the global pandemic of COVID-19, many people were afraid of an unknown disease without a cure, Travelers’ behavior has changed due to the government’s policy and people’s risk perception. The goal of this study is to analyze the data obtained through the survey and find the mode choice factors that influenced the selection of transportation changed due to COVID-19. The data needed for analysis were collected through a survey on the selection of transportation before and after the outbreak of COVID-19, at the peak time (the third pandemic in Korea from November 2020 to February 2021), and after the peak. In order to analyze the correlation between travel mode choice and individual tendency, bivariate probit model was developed. This study found that (1) due to the spread of COVID-19, private cars and private transportation are reduced, and public transportation is greatly reduced. (2) behavior changes were different depending on the type of work and working conditions. (3) behavior changes were different depending on the perception of public transportation. In conclusion, this study can prevent the spread of COVID-19 and help policy decision according to the travelers’ behavior in a different pandemic situation than before. © International Journal of Sustainable Building Technology and Urban Development.
ABSTRACT
This paper examines the determinants of changes in future public transport use in Scotland after the COVID-19 pandemic. An online questionnaire was distributed to 994 Scottish residents in order to identify travel habits, attitudes and preferences during the different phases of the COVID-19 outbreak and travel intentions after the pandemic. Quota constraints were enforced for age, gender and household income to ensure the sample was representative of the Scottish population. The respondents indicated that they anticipated they would make less use of buses and trains at the end of the pandemic. Over a third expect to use buses (36%) and trains (34%) less, whilst a quarter expect to drive their cars more. As part of the analysis, a random parameter bivariate probit model with heterogeneity in the means of random parameters was estimated to provide insights into the socio-demographic, behavioural and perceptual factors which might affect future public transport usage. The inclusion of random parameters allows for the potential effects of unobserved heterogeneity within the independent variables to be captured, whilst making allowances for heterogeneity in the means of the random parameters. The model estimation showed that several factors, including pre-lockdown travel choices, perceived risk of COVID-19 infection, household size and region significantly affected intended future use of public transport. In addition, several variables related to age, region, pre-lockdown travel choices and employment status resulted in random parameters. The current paper contributes to our understanding of the potential loss of demand for public transport and the consequences for future equitable and sustainable mobility. Our findings are highly relevant for transport policy when developing measures to strengthen the resilience of the public transport system during and after the pandemic.