Investigating Anticipated Changes in Post-Pandemic Travel Behavior: Latent Segmentation-Based Logit Modeling Approach Using Data From COVID-19 Era

Abstract

The unprecedented situation created by the COVID-19 pandemic in the year 2020 has drastically changed daily mobility patterns around the world. Various measures were implemented to prevent the transmission of the virus, which have resulted in short- and long-term impacts on the activity systems and daily travel. To capture the impacts of the pandemic on travel behaviors and activity systems, a web-based survey was designed and administered in April–May 2020 in Montreal, Canada. In addition to questioning on pre- and during COVID-19 behaviors, it included a section on how people expected to travel, telework, shop online, and so forth in the post-pandemic era. Using data from this survey, which gathered 1,620 completed questionnaires, this paper proposes insights into how people are planning to travel in a post-COVID-19 world using latent segmentation-based logit modeling technique. Three models are estimated to identify factors related to expected trip frequency, expected transit usage, and expected bike usage. Undertaking such modeling approach provides opportunity to understand different types of individuals’ preferential behaviors. This study probabilistically identifies two latent segments, suburbanite and urbanite people, and finds considerable heterogeneity across sample individuals. For example, urbanite people tend to increase their expected number of trips after COVID-19 if they have at least one bike in their household. Suburbanite people exhibit an opposite relationship, and they are more likely to keep their trip frequency the same as before. Findings of this study will assist decision makers in developing effective policy measures to better prepare for the changes in travel behaviors after COVID-19.