An Examination of the Adoption and Frequency of Part-Day and Full-Day Telework and the Impacts on Weekly Commute VMT

Term Start:

June 1, 2026

Term End:

May 31, 2027

Budget:

$75,000

Keywords:

Commuting, Telework, Travel Behavior

Thrust Area(s):

Data Modeling and Analytic Tools, Understanding User Needs

University Lead:

The University of Texas at Austin

Researcher(s):

Chandra Bhat

The COVID-19 pandemic has fundamentally reshaped work arrangements, creating long-term implications for transportation system performance, infrastructure planning, and demand management policy. While telework has received considerable attention, most existing research treats telework as a binary outcome, assuming that workers either commute to a workplace or remain at home for an entire day. This assumption does not reflect current behavior. A growing share of workers now engage in hybrid arrangements that include part-day telework, in which work is split between in-person and remote locations within a single day. This form of telework introduces additional flexibility in work scheduling and travel timing that is not represented in current transportation data systems or forecasting tools.

The distinction between part-day and full-day telework is important for transportation system performance. Full-day telework eliminates commute trips for a given day and directly reduces travel demand. In contrast, part-day telework typically retains commute travel but has the potential to shift its timing, redistribute trips across the day, and alter the temporal structure of travel demand. These effects are directly relevant to peak-period congestion, network reliability, and system efficiency. Despite these implications, major national data systems, including the American Community Survey and the National Household Travel Survey, do not adequately capture part-day telework behavior. As a result, current travel demand models may misrepresent both the level and timing of travel demand under hybrid work conditions, limiting their usefulness for evaluating transportation policies.

Motivated by the need to improve the behavioral realism and policy relevance of travel demand modeling in a post-pandemic environment, this study will examine the adoption and frequency of part-day and full-day telework using multi-day travel diary data from the 2023 Puget Sound Regional Household Travel Survey. These data provide detailed information on daily work location choices and activity participation over multiple days, enabling analysis of weekly travel patterns. The project will also establish defensible criteria for identifying part-day telework, addressing a key measurement issue that affects both survey design and model specification. The project will develop a joint econometric modeling framework that represents telework adoption, the number of part-day telework days, the number of full-day telework days, and commute distance. This joint structure is necessary to capture unobserved behavioral factors that simultaneously influence work arrangement choices and travel behavior, including preferences for flexibility, occupational constraints, and built environment characteristics. By explicitly modeling these interdependencies, the project will provide a more accurate representation of how work arrangements influence both the magnitude and timing of travel demand. In this regard, a central contribution of the project will be the translation of daily work location decisions into weekly commute VMT. By focusing on weekly activity-travel patterns, the project produces outputs that are directly relevant for evaluating congestion, system utilization, and the effectiveness of demand management strategies. The project will also examine variation in telework behavior across population groups and geographic contexts.

The expected contributions are both methodological and practical. The modeling framework will enable metropolitan planning organizations, state DOTs, and federal agencies to incorporate part-day telework into activity-based travel demand models. This capability is essential for improving the accuracy of forecasts used in infrastructure investment decisions and for evaluating policies aimed at improving system efficiency. In addition, the project will provide evidence on how hybrid work arrangements influence travel demand, supporting more effective design of congestion mitigation strategies, including pricing policies and flexible work programs.

Project scope

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