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
The rise of dockless bike sharing and the development of bike-only roads have facilitated a low-carbon transition in transportation. In this direction, Beijing opened the first bike-only road in 2019. Shortly after opening this bike road, the city encountered the COVID-19 pandemic. In this study, we evaluate the impacts of the first bike-only road and COVID-19 on the travel modal shift of the public and quantify the corresponding changes in carbon emissions. Using proposed integrated choice and latent variable models, we find that respondents who were previously low-carbon travelers were more likely to choose low-carbon traffic options and more sensitive to perceived instrumental usefulness of the bike sharing system than were high-carbon respondents. The bike lanes significantly increased the probability for all respondents to switch their travel mode to bike sharing. The COVID-19 pandemic significantly inhibited the high-carbon group from switching to bike sharing for both long- and short-distance trips, while the pandemic significantly promoted the low-carbon group to choose bike sharing for short-distance trips. However, there was no significant effect on this group by the pandemic for long-distance trips. Furthermore, we find that bike sharing can reduce carbon emissions by 41.55% and 33.43% for short- and long-distance trips, respectively. The overall emission reduction effect of bike sharing increased from 28.29% to 47.21% when the bike lane was introduced;whereas the COVID-19 pandemic decreased bike sharing emission reduction from 43.74% before to 29.87% during the pandemic. Our research reveals that the bike sharing system plays both an alternative role to other traffic modes for short-distance traffic and a complementary role to public transport for long-distance traffic. © 2022 Elsevier Ltd
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
Shared mobility is growing rapidly and changing the mobility landscape. The COVID-19 pandemic has complicated travel mode choice behavior in terms of shared mobility, but the evidence on this impact is limited. To fill this gap, this paper first designs a stated preference survey to collect mode choice data before and during the pandemic. Different shared mobility services are considered, including ride hailing, ride sharing, car sharing, and bike sharing. Then, latent class analysis is used to divide the population in terms of their attitudes toward shared mobility. Nested logit models are applied to compare travel mode choice behavior during the two periods. The results suggest that shared mobility has the potential to avoid the high transmission risk of public transport and alleviate the intensity of private car use in the COVID-19 context, but this is limited by anxiety about shared spaces. As the perceived severity of the pandemic increases, preference for ride hailing and ride sharing decreases, and a price discount for ride hailing is more effective than that for ride sharing at maintaining the ridership despite the impact of COVID-19. These findings contribute to understanding the change in travel demand and developing appropriate strategies for shared mobility services to adapt to the pandemic.
Subject(s)
COVID-19 , Beijing , COVID-19/epidemiology , Choice Behavior , Humans , Pandemics , TravelABSTRACT
To analyze the impact of COVID-19 on the travel mode choice behavior with diverse shared mobility services, this study designed the stated preference (SP) questionnaire for the multi-modal transportation system which include conventional travel modes, ride hailing, ride sharing, car sharing, and bike sharing. The mixed Logit models with panel data were proposed to investigate the travel mode choices before and during COVID-19. The influence differences of explanatory variables are compared, and the joint effects of perceived pandemic severity and mode choice inertia are examined. Based on the elasticity analysis, the mode choice preferences are predicted corresponding to different management policies under COVID-19 pandemic. The results indicate that the perception to pandemic severity has significant impacts on the ridership of ride sharing and car sharing, and the mode choice inertia obviously affects the usage of ride hailing, car sharing, and bike sharing. When the perceived pandemic severity reduces to 30%~50%, the strategy of increasing parking charge to 1.6~3.0 times would reduce the usage of private car to pre-pandemic condition, and the car sharing with lower close contact risk could become a main substitute. When the perceived pandemic severity is higher than 60%, the strategy of increasing the travel safety of ride sharing to 1.4~3.6 times would improve the ridership. Copyright © 2022 by Science Press.
ABSTRACT
The year 2020 was characterized by a marked shift in daily travel patterns due to the COVID-19 pandemic. While we know that overall travel decreased, less is known about modal shift among those who continued to travel during the pandemic or about the impact of these travel-behaviour changes on transport-related greenhouse gas emissions. Focusing on a university setting and drawing from a travel survey conducted in Fall 2020 in Montreal, Canada (n=3,358), this study examines modal shifts and quantifies greenhouse gas emissions at three time periods in the year 2020: pre-pandemic, early pandemic, and later pandemic. The pandemic resulted in a sharp reduction in travel to campus. Among those who continued to travel to campus (n=1,580), car-to-final destination mode share almost tripled at the start of the pandemic. The largest modal shift seen was the transition from walking, cycling, and transit, to driving at the beginning of the pandemic. Reductions in overall travel resulted in lower overall transport-related greenhouse gas emissions. However, if modal changes persist once students, staff, and academics return to campus, the transport carbon footprint is projected to increase above pre-pandemic levels. These results highlight the importance of putting in place policies that support a return to sustainable modes as universities and businesses reopen for in-person activities.
ABSTRACT
Travel activities and travel behaviors have been greatly affected by the outbreak of Covid-19. Facing the change of individuals' travel choices, policymakers have to make an appropriate response to mitigate negative consequences. This paper aims to explore how the COVID-19 would impact travel mode choice and the intention of car purchase. The data was collected from a large-scale survey conducted in June 2020 after the highest point. Random utility maximization (RUM), random regret minimization (RRM) and generalized regret minimization (GRRM) are employed to examine the effects of various factors on mode choice behaviors. The estimation results reveal that regret aversion psychology doesn't have a dominant proportion of decision choices, even if the congested condition of the mass mobility plays a significant role in the consideration of decision-making. Combined with the statistical results from the official departments, we concluded that public transport displays a great propensity on the long trip, and meanwhile, the industry of ride-hailing services has shocked sharply. In terms of the intention of traffic tool purchase, carless people prefer to buy electric two-wheel vehicles rather than automobiles. The research findings and the contribution to policy implications give assistance to authority in understanding citizens' travel mode preferences under the impact of COVID-19.