Shifts in Work Arrangements and Travel Behavior: A Longitudinal Analysis of Heterogeneous Effects of Teleworking on Commuting and Non-Commuting Travel Based on Seattle Commute Surveys

Term Start:

June 1, 2026

Term End:

September 1, 2027

Budget:

$150,000

Keywords:

Commuting, Machine Learning, Telework

Thrust Area(s):

Data Modeling and Analytic Tools, Understanding User Needs

University Lead:

University of Washington

Researcher(s):

Qing Shen

Following the official end of the COVID-19 pandemic in May 2023, employer policies regarding work arrangements have continued to change dynamically. This proposed project will draw on unique data from the 2022 and 2024 Seattle Commute Surveys to analyze how shifts in work arrangements from the pandemic to the post-pandemic era heterogeneously shape employees’ commuting and non-commuting travel frequencies and mode choices, using econometric models, machine learning models, and explainable AI methods. We will examine individual employees’ post-pandemic work arrangements and patterns of change in commuting and non-commuting travel. The research will address three primary questions: 1) How have employees’ work arrangements and travel patterns shifted from the pandemic to the post-pandemic era? 2) What is the relationship between work arrangement shifts and commuting mode choices? In what ways does the relationship vary across commuting distance strata and residential location types? 3) How are work arrangement shifts connected to non-commuting travel frequency and mode choices? Are these connections heterogeneous across employees’ residential locations and commuting patterns? By employing econometric and AI models, this study aims to generate critical insights for transportation demand management and policy-making.

Project scope

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