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Unveiling daily activity pattern differences between telecommuters and commuters using human mobility motifs and sequence analysis

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  • Su, Rongxiang
  • McBride, Elizabeth C.
  • Goulias, Konstadinos G.

Abstract

This paper demonstrates the use of motif and sequence analysis in tandem to analyse differences and commonalities between telecommuters and usual commuters. In terms of substantive findings, telecommuters are by far more diverse in their allocation of time to places, activities, and travel. Approximately 20% of telecommuters stay at home all day during a workday, while only 8% of commuters do. Telecommuters that have at least one trip during their workday accrue more vehicle miles travelled and number of trips than their commuter counterparts. However, they travel less driving alone and tend to have more complex schedules visiting more locations. Within telecommuters and commuters, however, we have substantial variation in activity participation and travel captured by the combination of motifs and sequence analysis. As expected, a substantial proportion of commuters display morning and afternoon peaks of arriving at and departing from work, and telecommuters do not show this pattern. In addition, telecommuters do not only perform work tasks from home. Instead, during a day a high percentage travel to a variety of locations to either visit customers and/or use their spatio-temporal schedule flexibility to perform work tasks from locations other than home. In contrast, more than 80% of commuters perform work at their workplace. In addition, a slightly higher proportion of telecommuters function as the designated driver escorting other people to their activity locations.

Suggested Citation

  • Su, Rongxiang & McBride, Elizabeth C. & Goulias, Konstadinos G., 2021. "Unveiling daily activity pattern differences between telecommuters and commuters using human mobility motifs and sequence analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 106-132.
    • Handle: RePEc:eee:transa:v:147:y:2021:i:c:p:106-132
    DOI: 10.1016/j.tra.2021.03.002
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    References listed on IDEAS

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    Cited by:

    1. Shi, Shuyang & Wang, Lin & Wang, Xiaofan, 2022. "Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    2. Handy, Susan L & Volker, Jamey M. B. & Hosseinzade, Reyhane, 2024. "Assessing the Effectiveness of Potential Vehicle-Miles-Traveled (VMT) Mitigation Measures," Institute of Transportation Studies, Working Paper Series qt1pf307sp, Institute of Transportation Studies, UC Davis.
    3. Shi, Hui & Su, Rongxiang & Xiao, Jingyi & Goulias, Konstadinos G., 2022. "Spatiotemporal analysis of activity-travel fragmentation based on spatial clustering and sequence analysis," Journal of Transport Geography, Elsevier, vol. 102(C).
    4. Konstantinos Christopoulos & Konstantinos Eleftheriou & Peter Nijkamp, 2022. "The role of pre-pandemic teleworking and E-commerce culture in the COVID-19 dispersion in Europe," Letters in Spatial and Resource Sciences, Springer, vol. 15(1), pages 1-16, April.
    5. Rafiq, Rezwana & McNally, Michael G. & Sarwar Uddin, Yusuf & Ahmed, Tanjeeb, 2022. "Impact of working from home on activity-travel behavior during the COVID-19 Pandemic: An aggregate structural analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 35-54.
    6. Nicholas S. Caros & Jinhua Zhao, 2022. "Preparing urban mobility for the future of work," Papers 2201.01321, arXiv.org.
    7. Shi, Hui & Goulias, Konstadinos G., 2024. "Year-to-year time allocation and spatial structure of Americans’ daily schedules from 2019 to 2022 and a detailed analysis of the stay-at-home all-day patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).
    8. Wang, Xinyi & Mokhtarian, Patricia L., 2024. "Examining the treatment effect of teleworking on vehicle-miles driven: Applying an ordered probit selection model and incorporating the role of travel stress," Transportation Research Part A: Policy and Practice, Elsevier, vol. 186(C).
    9. Wang, Xize & Renne, John L., 2023. "Socioeconomics of Urban Travel in the U.S.: Evidence from the 2017 NHTS," SocArXiv cdw2y, Center for Open Science.
    10. Marc-Edouard Schultheiss, 2022. "Assessment of the Bus Transit Network: A Perspective from the Daily Activity-Travel Organization of Travelers," Sustainability, MDPI, vol. 14(4), pages 1-20, February.
    11. Somayeh Dodge & Trisalyn A. Nelson, 2023. "A framework for modern time geography: emphasizing diverse constraints on accessibility," Journal of Geographical Systems, Springer, vol. 25(3), pages 357-375, July.
    12. Asmussen, Katherine E. & Mondal, Aupal & Bhat, Chandra R. & Pendyala, Ram M., 2023. "On modeling future workplace location decisions: An analysis of Texas employees," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    13. Rongxiang Su & Somayeh Dodge & Konstadinos G. Goulias, 2022. "Understanding the impact of temporal scale on human movement analytics," Journal of Geographical Systems, Springer, vol. 24(3), pages 353-388, July.
    14. Xize Wang & John L. Renne, 2023. "Socioeconomics of Urban Travel in the U.S.: Evidence from the 2017 NHTS," Papers 2303.04812, arXiv.org.
    15. Su, Rongxiang & Goulias, Konstadinos, 2023. "Untangling the relationships among residential environment, destination choice, and daily walk accessibility," Journal of Transport Geography, Elsevier, vol. 109(C).
    16. Bouzaghrane, Mohamed Amine & Obeid, Hassan & Villas-Boas, Sofia B. & Walker, Joan, 2024. "Influence of telecommuting on out-of-home time use and diversity of locations visited: Evidence from the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).

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