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Author ORCID Identifier

https://orcid.org/0009-0002-4425-5999 

Date Available

5-1-2027

Year of Publication

2026

Document Type

Master's Thesis

Degree Name

Master of Science in Civil Engineering (MSCE)

College

Engineering

Department/School/Program

Civil Engineering

Faculty

Gregory D. Erhardt

Faculty

Mei Chen

Abstract

The increase in hybrid and always remote work since the COVID-19 pandemic has uncertain effects on vehicle miles traveled (VMT). Intuitively, hybrid and always remote work should reduce VMT since work trips are eliminated. However, eliminating work trips could result in newly generated VMT during remote work days, thus overstating the VMT reduction. Past efforts to quantify these effects are limited by data that do not capture a full week of travel, do not capture the changes following the COVID-19 pandemic, and/or do not account for factors that influence both selection into work arrangement and weekly VMT, leading to biased estimates on the effect of hybrid and always remote work. To overcome these limitations, we use an ordered probit switching regression (OPSR) model to analyze week-long travel diary data from the Minneapolis-St. Paul region for 2019, 2021, and 2023. We find that switching from always in-person to hybrid and always remote work decreases weekly VMT by 10.4% and 25.4%, respectively. As jurisdictions adopt VMT reduction goals, these results show that switching to always remote work is a more effective strategy than switching to hybrid work. We also quantify rebound effects – the percentage of the decrease in weekly work tour VMT offset by increases in weekly non-work tour VMT – and find that 55% of hybrid and 57% always remote workers’ decreases in weekly work tour VMT are offset by increased non-work tour VMT, illustrating that rebound effects should be considered when evaluating remote work arrangements in VMT reduction strategies.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2026.187

Archival?

Archival

Funding Information

This material is based upon work supported by the National Science Foundation under Award No. 2425092.

Available for download on Saturday, May 01, 2027

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