Are Telecommuters’ and Non-Telecommuters’ Daily Time-Use Behaviors Different? An Episode-Level Model for Non-Mandatory Activities

Shivam Khaddar · Mahmudur Rahman Fatmi · Trevor Nikodym

13 citations0/0 files storedTransportation Research Record Journal of the Transportation Research Board

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Transportation Planning and Optimization Work-Family Balance Challenges Human Mobility and Location-Based Analysis

Abstract

Telecommuters (individuals working from home), and non-telecommuters (individuals working at the workplace) might have significantly different daily time-use patterns. For example, telecommuters might be able to engage in more episodes of non-mandatory activities such as recreation and pick-up/drop-off of children, as many telecommuters have a relatively flexible work arrangement in relation to their work location and schedule. This study aims to explore the non-mandatory activity engagement and duration decisions of these two worker profiles at a disaggregated-episode level. To do so, a multiple discrete continuous extreme value with ordered preferences (MDCEV-OP) model is adopted, using the 2018 travel survey data from the Central Okanagan region of British Columbia, Canada. This model accounts for multiple occurrences of each activity type (i.e., episodes) along with their corresponding durations. In doing so, the model adopts a logical ordering ensuring that the jth episode is not predicted without the occurrence of the ( j–1)th episode of an activity type. The model results reveal that telecommuters are more likely to engage in higher episodes of non-mandatory activities, compared with non-telecommuters. For instance, female teleworkers are found to be more likely to participate in higher episodes of activities associated with household-related responsibilities such as shopping. In contrast, female non-telecommuters are associated with participating in more episodes of personal business-related activities. The findings of this study provide important behavioral insights into the activity-time-use patterns of telecommuters and non-telecommuters, which can be utilized to develop more effective and equitable travel demand management plans, policies, and models.

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References

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2026An integrated mutual-information clustering and deep neural network framework for forecasting travel behaviour under flexible working arrangements 2024Scientometric literature review: Effects of Work from Home (WFH) on transportation system 2024Investigating the impacts of telecommuting on the spatial, temporal, and modal distribution of travel using an agent-based transport simulation model

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