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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
20.06.2024
DOI
https://doi.org/10.7307/ptt.v36i3.470
ISSUE
2024 (Vol 36), Issue 3
LICENSE
Copyright (c) 2024 Mahmut Esad ERGIN
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

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

Authors:Mahmut Esad ERGIN

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.

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

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