The Effects of the COVID-19 Pandemic on the Modal Shifting Utilising a Latent Class Choice Model with Covariates

Authors

  • Mahmut Esad ERGIN Department of Civil Engineering, Istanbul Aydin University

DOI:

https://doi.org/10.7307/ptt.v36i3.470

Keywords:

countermeasure, covariates, latent class model, pandemic, survey analysis, modal shifting

Abstract

The COVID-19 pandemic has posed significant challenges to global public health organisations and governments, leading to countermeasures like hand sanitizer availability, social distancing, and mandatory face mask wearing, which have disrupted the public transportation sector and impacted the virus spread. Anticipating the effects of circumstances like a pandemic on mobility is essential for operators and managers of public transportation systems to effectively and safely manage the system. In this study, the measures taken during the pandemic, such as those mentioned above, were considered as indicators in the latent class model (LCM) for modal shifting. The model incorporates sociodemographic variables as covariates to understand their impact on modal shifting from public transport to private cars. An online survey with 53,973 valid responses was conducted in Istanbul, Turkiye. As a result of the LCM with covariates, two-latent-class model, the best fit among models ranging from two to six latent classes, emerged. Class-1 participants show increased sensitivity to the pandemic, shifting to private mode, while Class-2 participants are less concerned and tend to maintain their existing mode. The model suggests using LCM with covariates to estimate the modal shift from public transportation to private cars in any given situation.

References

Rajab K, et al. Forecasting COVID-19: Vector Autoregression-Based Model. Arab J Sci Eng. 2022;47:6851–6860. DOI: 10.1007/s13369-021-06526-2.

World Health Organization (WHO). Timeline of WHO’s Reponse to Covid-19. Retrieved 30th July 2022. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline# [Accessed 12th April 2024].

Rizzo A, et al. Effect of individual behavior on epidemic spreading in activity-driven networks. Phys. Rev. E. 2014;90(4):042801. DOI: 10.1103/PhysRevE.90.042801.

Yan QL, et al. Impact of individual behaviour change on the spread of emerging infectious diseases. Stat. Med. 2018;37(6):948–969. DOI: 10.1002/sim.7548.

De Haas M, et al. How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands. Transportation Research Interdisciplinary Perspectives. 2020;6:100150. DOI: 10.1016/j.trip.2020.100150.

Beck J, Hensher, DA. Insights into the impact of COVID-19 on household travel and activities in Australia – The early days under restrictions. Transport Policy. 2020;96:76–93. DOI: 10.1016/j.tranpol.2020.07.001.

Sameni MK, et al. Will modal shift occur from subway to other modes of transportation in the post-corona world in developing countries?. Transport Policy. 2021;111:82–89. DOI: 10.1016/j.tranpol.2021.07.014.

Lee KS, Eom JK. Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility. Transportation. 2023. DOI: 10.1007/s11116-023-10392-2.

Ergin ME, et al. The evaluation of the impacts on traffic of the countermeasures on pandemic in Istanbul. International Road Federation World Meeting & Exhibition. 2022. Springer. DOI: 10.1007/978-3-030-79801-7_66.

Circella, G, Dominguez-Faus, R. Impacts of the COVID-19 pandemic on transportation use: Updates from UC Davis behavioral study. 4th August 2020. Webinar: UC Davis Institute of Transport Studies, 3 Revolutions Program. https://its.ucdavis.edu/blog-post/impacts-of-the-covid-19-pandemic-on-transportation-use-updates-from-uc-davis-behavioral-study/ [Accessed 12th April 2024].

Chen C, et al. Investigating the effectiveness of COVID-19 pandemic countermeasures on the use of public transport: A case study of The Netherlands. Transport Policy. 2022;117:98–107. DOI: 10.1016/j.tranpol.2022.01.005.

Bhaduri E, et al. Modelling the effects of COVID-19 on travel mode choice behaviour in India. Transportation Research Interdisciplinary Perspectives. 2020;8;100273. DOI: 10.1016/j.trip.2020.100273.

Katrakazas C, et al. A descriptive analysis of the effect of the COVID-19 pandemic on driving behavior and road safety. Transportation Research Interdisciplinary Perspectives. 2020;7:100186. DOI: 10.1016/j.trip.2020.100186.

Ren M, et al. Impact of the COVID-19 pandemic on travel behavior: A case study of domestic inbound travelers in Jeju, Korea. Tourism Management. 2022;92:104533. DOI: 10.1016/j.tourman.2022.104533.

Cheng YT, et al. How did the COVID-19 pandemic impact the location and duration of work activities? A latent class time-use study. Findings. 2022.

Abouelela M, et al. Exploring COVID-19 pandemic potential impacts on students’ school travel behavior. Transportation Letters. 2023;1–15. DOI: 10.1080/19427867.2023.2187334.

Sanhita Das S, et al. Impact of COVID-19: A radical modal shift from public to private transport mode. Transport Policy, 2021;109:1–11. DOI: 10.1016/j.tranpol.2021.05.005.

Chen X, et al. Exploring essential travel during COVID-19 quarantine: Evidence from China. Transport Policy. 2021;111:90–97. DOI: 10.1016/j.tranpol.2021.07.016.

Hagenaars JA, McCutcheon AL. Applied latent class analysis. Cambridge University Press; 2002.

Karnowski V. Latent class analysis. The International Encyclopedia of Communication Research Methods. 2017. DOI: 10.1002/9781118901731.iecrm0130.

Vermunt JK. Latent class modeling with covariates: Two improved three-step approaches. Political Analysis. 2010;18:450–469. DOI: 25792024.

Bucsky P. Modal share changes due to COVID-19: The case of Budapest. Transportation Research Interdisciplinary Perspectives. 2020;8;100141. DOI: 10.1016/j.trip.2020.100141.

Kaufman SM, et al. Transportation during Corona virus in New York City. Rudin Center for Transportation Policy & Management, NYU; 2020. https://wagner.nyu.edu/impact/research/publications/transportation-during-coronavirus-nyc [Accessed 12th April 2024].

Jenelius E, Cebecauer M. Impacts of COVID-19 on public transport ridership in Sweden: Analysis of ticket validations, sales and passenger counts. Transportation Research Interdisciplinary Perspectives. 2020;8. DOI: 10.1016/j.trip.2020.100242.

Yildizhan F, Bilgic S. The financial impact of the COVID-19 pandemic on public transportation and sustainable policy recommendations: A case study of Eskisehir. Gazi University Journal of Science. 2023;36(2):573–590. DOI: 10.35378/gujs.1022067.

Shelat S, et al. Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands. Transportation Research Part A. 2022;159:357–371. DOI: 10.1016/j.tra.2022.03.027.

Pozo RF, et al. Data-driven analysis of the impact of COVID-19 on Madrid’s public transport during each phase of the pandemic. Cities. 2022:127. DOI: 10.1016/j.cities.2022.103723.

Medlock KB, et al. COVID-19 and the value of safe transport in the United States. Sci Rep. 2021;11:21707. DOI: 10.1038/s41598-021-01202-9.

Loa P, et al. Exploring the impacts of the COVID-19 pandemic on modality profiles for non-mandatory trips in the Greater Toronto Area. Transport Policy. 2021:110;71–85. DOI: 10.1016/j.tranpol.2021.05.028.

Gao Y, et al. Understanding patients heterogeneity in healthcare travel and hospital choice - A latent class analysis with covariates. Journal of Transport Geography. 2023;110:103608. DOI: 10.1016/j.jtrangeo.2023.103608.

Singh J, et al. Change in departure time for a train trip to avoid crowding during the COVID-19 pandemic: A latent class study in the Netherlands. Transportation Research Part A. 2023:170;103628. DOI: 10.1016/j.tra.2023.103628.

Lee Y, de Vos J. Who would continue to work from home in Hong Kong as the COVID-19 pandemic progresses? Transportation Research Part D. 2023;120:103753. DOI: 10.1016/j.trd.2023.103753.

Ma X, et al. Non-commuting intentions during COVID-19 in Nanjing, China: A hybrid latent class modeling approach. Cities, 2023;137:104341. DOI: 10.1016/j.cities.2023.104341.

Turkish Statistical Institute (TSI). Address based population registration system results, 2022. 2023. https://rb.gy/jpmqp [Accessed 12th April 2024].

Barrett A, et al. Changes in transportation during the COVID-19 pandemic: Results from a survey of middle-aged and older Floridians. Innovation in Aging. 2021:5;126–127. DOI: 10.1093/geroni/igab046.488.

Liang X, et al. City planning and transportation system under the normalization of COVID-19 pandemic based on network survey in the era of big data. Journal of Physics: Conference Series, 1992. 2021:4;042074. DOI: 10.1088/1742-6596/1992/4/042074.

Sureshbabu K, et al. Exploring the use of public transportation among older adults during the COVID-19 pandemic: A national survey. Report No: 10.31979/mti.2022.2204, 2022. https://transweb.sjsu.edu/research/2204-COVID-19-Aged-Public-Transportation-Accessibility [Accessed 12th April 2024].

Linzer DA, Lewis JB. poLCA: An R package for polytomous variable latent class analysis. Journal of Statistical Software. 2011;42(10). DOI: 10.18637/jss.v042.i10.

Shah H, et al. What is your shopping travel style? Heterogeneity in US households’ online shopping and travel. Transportation Research Part A. 2021;153:83–98. DOI: 10.1016/j.tra.2021.08.013.

Weller BE, et al. Latent class analysis: A guide to best practice. Journal of Black Psychology. 2020;46(4):287–311. DOI: 10.1177/0095798420930932.

Ben-Akiva M, et al. Integration of choice and latent variable models. In: Mahmassani H. (ed) Perpetual motion: Travel behavior research opportunities and application challenges. Pergamon, Oxford; 2002.

Lazarsfeld PF, Neil WH. Latent structure analysis. Boston: Houghton Mill; 1968.

David S, et al. Algorithm 717: Subroutines for maximum likelihood and quasi-likelihood estimation of parameters in nonlinear regression models. ACM Transactions on Mathematical Software. 1993;19(1):109–130. DOI: 10.1145/151271.151279.

Hess S. Allowing for heterogeneous decision rules in discrete choice models: An approach and four case studies. Transportation. 2012;39:565–591. DOI: 10.1007/s11116-011-9365-6.

Dillingham, RT. A latent-class discrete-choice model to demonstrate how course attributes and student characteristics influence demand for economics electives: The challenge to increase enrollment. Open Access Master's Thesis. Michigan Technological University; 2016. DOI: 10.37099/mtu.dc.etdr/262.

Tein J, et al. Statistical power to detect the correct number of classes in latent profile analysis. Struct. Equ. Model. Multidiscip. J. 2013;20(4):640–657. DOI: 10.1080/10705511.2013.824781.

Lee H, et al. Factors affecting heterogeneity in willingness to use e-scooter sharing services. Transportation Research Part D: Transport and Environment. 2021;92:102751. DOI: 10.1016/j.trd.2021.102751.

Downloads

Published

20-06-2024

How to Cite

ERGIN, M. E. (2024). The Effects of the COVID-19 Pandemic on the Modal Shifting Utilising a Latent Class Choice Model with Covariates. Promet - Traffic&Transportation, 36(3), 399–414. https://doi.org/10.7307/ptt.v36i3.470

Issue

Section

Articles