The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Exploring the impacts of COVID-19 on travel behavior and mode preferences
Open accessAbstract
Various measures were recommended or imposed by the governments to control the spread of COVID-19. Travel behaviors are significantly influenced due to such measures. However, people have various travel needs ranging from grocery shopping to work. This study examines the changes that occurred in travel behavior due to the COVID-19 pandemic. Data were collected through an online questionnaire survey that included questions on trip purpose, mode choice, distance traveled, and frequency of trips before and during COVID-19. 1203 responses were collected from various countries around the world. Results explained that trip purpose, mode choice, distance traveled, and frequency of trips for the primary travel were significantly different before and during the pandemic. Further, the majority of trips were made for shopping during the pandemic. There was a significant shift from public transport to private transport and non-motorized modes. People placed a higher priority on the pandemic related concerns while choosing a mode during the pandemic as compared to the general concerns. Gender, car ownership, employment status, travel distance, the primary purpose of traveling, and pandemic-related underlying factors during COVID-19 were found to be significant predictors of mode choice during the pandemic. Outcomes of this study could be useful in transport planning and policymaking during pandemics based on the travel needs of people. In particular, government authorities could utilize such knowledge for planning smart and partial lockdowns. Service providers, e.g., taxi companies and retailers, could use such information to better plan their services and operations.
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Cites
The effect of human mobility and control measures on the COVID-19 epidemic in China
Kraemer, M. U. G., Yang, C.-H., Gutierrez, B., Wu, C.-H., Klein, B., Pigott, D. M., Open COVID-19 Data Working Group, du Plessis, L., Faria, N. R., Li, R., et al. (2020) · Science · doi:10.1126/science.abb4218 · Cited by 2,950
Understanding and Using Factor Scores: Considerations for the Applied Researcher
DiStefano, C., Zhu, M., & Mîndrilă, D. (2020) · doi:10.7275/da8t-4g52 · Cited by 1,614
COVID-19 exacerbating inequalities in the US
Dorn, A. V., Cooney, R. E., & Sabin, M. L. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30893-x · Cited by 1,315
Modelling the influence of human behaviour on the spread of infectious diseases: a review
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COVID-19 pandemic and environmental pollution: A blessing in disguise?
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The effect of COVID-19 and subsequent social distancing on travel behavior
De Vos, J. (2020) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2020.100121 · Cited by 961
Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020
Goolsbee, A., & Syverson, C. (2021) · Journal of Public Economics · doi:10.1016/j.jpubeco.2020.104311 · Cited by 749
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Community Responses during Early Phase of COVID-19 Epidemic, Hong Kong
Kwok, K. O., Li, K. K., Chan, H. H. H., Yi, Y. Y., Tang, A., Wei, W. I., & Wong, S. Y. S. (2020) · Emerging Infectious Diseases · doi:10.3201/eid2607.200500 · Cited by 582
Quantitative Methods for Analysing Travel Behaviour of Individuals
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Cited by
Lee, S., Ko, E., Jang, K., & Kim, S. (2023) · Cities · doi:10.1016/j.cities.2023.104223 · Cited by 37
Metadata
- Authors
- Abdullah, M., Dias, C., Muley, D., & Shahin, M.
- Venue
- Transportation Research Interdisciplinary Perspectives
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 8
- Pages
- 100255
- ISSN
- 2590-1982 (ISSN-L)
- OpenAlex
- W3095291149
- Cited by
- 678
- References
- 42
- FWCI
- 108.60
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.