How has the COVID-19 pandemic affected the use of ride-sourcing services? An empirical evidence-based investigation for the Greater Toronto Area

Loa, Patrick; Hossain, Sanjana; Liu, Yicong; Habib, Khandker Nurul

doi:10.1016/j.tra.2021.11.013

Cited by 25 publications

(16 citation statements)

References 40 publications

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“…Besides the analyses based on mobility records, surveybased work also plays an important role in understanding how the COVID-19 pandemic could reshape the future of OMS. It was widely reported that people's opinion of different mobility choices has changed due to the pandemic (Nikiforiadis et al 2020;Awad-Núñez et al 2021;Bhaduri et al 2020;Shamshiripour et al 2020;Rasheed Gaber and Elsamadicy 2021;Loa et al 2022). For instance, the general willingness to use bikesharing services was unchanged (Nikiforiadis et al 2020) or became higher (Awad-Núñez et al 2021) than the pre-COVID period.…”

Section: Covid-19 Impact To Human Mobilitymentioning

confidence: 99%

“…For instance, the general willingness to use bikesharing services was unchanged (Nikiforiadis et al 2020) or became higher (Awad-Núñez et al 2021) than the pre-COVID period. People would stop using the ridehailing and public transit services due to the risks of human-to-human contact (Bhaduri et al 2020;Shokouhyar et al 2021;Rasheed Gaber and Elsamadicy 2021;Loa et al 2022) and telecommuting was likely to continue and thus reduce the travel demand (Shamshiripour et al 2020).…”

Section: Covid-19 Impact To Human Mobilitymentioning

confidence: 99%

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Understanding the Recovery of On-Demand Mobility Services in the COVID-19 Era

Lei

Ukkusuri

2022

J. Big Data Anal. Transp.

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The COVID-19 pandemic and its related events (e.g., lockdown policies, vaccine distributions) have caused disruptive changes in travel patterns and urban mobility services. Cities need to understand the impacts of these factors on mobility activities for taking effective actions to restore/reform urban transportation systems and prepare for future shocks. In this study, we investigate the correlations between the COVID-19 related factors and the usage of on-demand mobility services (OMS, i.e., street-hailing, ride-hailing, and bike-sharing) through a two-step framework. In the first step, we construct lowdimensional representations, called mobility signals, of multivariate mobility data which provide a temporal understanding of the variation of trips across different modes. Then the Bayesian structural time series model is utilized to estimate the regression coefficients and inclusion probability of different time-varying factors including COVID-19 cases, policies, and vaccination rates in predicting each mobility signal. This framework is adopted in New York City (NYC) and Chicago, two example cities that have been significant affected by COVID-19 disruptions and that have comprehensive on-demand mobility services. The results suggest an asymmetrical influence of COVID-19 related policies to the usage of OMS: the mobility/ business restrictions can trigger fast and consistent decrease of ridership, but lifting these restrictions does not result in a fast rebound. Our analyses further uncovers the heterogeneity of spatial impacts of different COVID-19 related policies. A one-year prediction of OMS usage is conducted and the results suggest a highly uncertain future of the ride-hailing and street-hailing services, and relatively stable bike-sharing usage in the near future.

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Section: Covid-19 Impact To Human Mobilitymentioning

confidence: 99%

Section: Covid-19 Impact To Human Mobilitymentioning

confidence: 99%

Understanding the Recovery of On-Demand Mobility Services in the COVID-19 Era

Lei

Ukkusuri

2022

J. Big Data Anal. Transp.

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“…Passenger-trips by all commercial airlines fell 50% compared to 2019 flights (OAG Aviation Worldwide 2021). Public transit fell sharply everywhere (Beck and Hensher 2020) largely because people hesitated to use shared modes (Dean and Zuniga-Garcia 2022;Loa et al 2022). Although the pandemic's impacts on daily travel behavior have been extensively explored (Abdullah et al 2020;De Vos 2020;Habib et al 2021), studies about impacts on long-distance (LD) travel are lacking.…”

Section: Questionsmentioning

confidence: 99%

Long-Distance Travel Impacts of COVID-19 Across the United States

2022

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Using over a thousand Americans’ population-weighted responses to a long-distance travel survey, this paper examines reductions in trips over 75-miles (one-way) in 2020, during the pandemic, versus behaviors in 2019. Negative binomial models of trip counts suggest that people age 25 to 64 took 0.20 fewer annual long-distance business trips during the pandemic, but people age 65 and older took 0.45 fewer business and 0.57 fewer non-business long-distance trips, on average. Household income was not a key predictor for long-distance non-business-trip-making during the pandemic, but was important in predicting long-distance trip rates for business purposes (both before and during the pandemic) and for non-business trips pre-pandemic.

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“…The perceived severity of the pandemic has complicated travel mode choice behavior in terms of shared mobility. The ridership of shared mobility was limited by home quarantines and anxiety regarding entering shared spaces [ 10 ]. However, new mobility services may play a vital role in post-pandemic travel industry recovery with advantages such as reliability, sustainability, and less in-vehicle crowding [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

The Impact of COVID-19 on Travel Mode Choice Behavior in Terms of Shared Mobility: A Case Study in Beijing, China

Zhang

Shao

Wang

et al. 2022

IJERPH

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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.

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How has the COVID-19 pandemic affected the use of ride-sourcing services? An empirical evidence-based investigation for the Greater Toronto Area

Cited by 25 publications

References 40 publications

Understanding the Recovery of On-Demand Mobility Services in the COVID-19 Era

Understanding the Recovery of On-Demand Mobility Services in the COVID-19 Era

Long-Distance Travel Impacts of COVID-19 Across the United States

The Impact of COVID-19 on Travel Mode Choice Behavior in Terms of Shared Mobility: A Case Study in Beijing, China

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