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Modeling and Analysis of Excess Commuting with Trip Chains

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  • Yujie Hu
  • Xiaopeng Li

Abstract

Commuting, like other types of human travel, is complex in nature, such as trip-chaining behavior involving making stops of multiple purposes between two anchors. According to the 2001 National Household Travel Survey, about half of weekday U.S. workers made a stop during their commute. In excess commuting studies that examine a region’s overall commuting efficiency, commuting is, however, simplified as nonstop travel from homes to jobs. This research fills this gap by proposing a trip-chaining-based model to integrate trip-chaining behavior into excess commuting. Based on a case study of the Tampa Bay region of Florida, this research finds that traditional excess commuting studies underestimate both actual and optimal commute and overestimate excess commuting. For chained commuting trips alone, for example, the mean minimum commute time is increased by 70 percent from 5.48 minutes to 9.32 minutes after trip-chaining is accounted for. The gaps are found to vary across trip-chaining types by a disaggregate analysis by types of chain activities. Hence, policymakers and planners are cautioned with regards to omitting trip-chaining behavior in making urban transportation and land use policies. In addition, the proposed model can be adopted to study the efficiency of nonwork travel.

Suggested Citation

  • Yujie Hu & Xiaopeng Li, 2021. "Modeling and Analysis of Excess Commuting with Trip Chains," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 111(6), pages 1851-1867, September.
    • Handle: RePEc:taf:raagxx:v:111:y:2021:i:6:p:1851-1867
    DOI: 10.1080/24694452.2020.1835461
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    Citations

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    Cited by:

    1. Yujie Hu, 2022. "The unequal commute: Comparing commuting patterns across income and racial worker subgroups," Environment and Planning A, , vol. 54(3), pages 437-440, May.
    2. Jing, Yue & Hu, Yujie, 2022. "The unequal commuting efficiency: A visual analytics approach," Journal of Transport Geography, Elsevier, vol. 100(C).
    3. Bailey Glover & Liang Mao & Yujie Hu & Jiawen Zhang, 2022. "Enhancing the Retail Food Environment Index (RFEI) with Neighborhood Commuting Patterns: A Hybrid Human−Environment Measure," IJERPH, MDPI, vol. 19(17), pages 1-15, August.
    4. Hu, Yujie & Sherlock, Phillip & Huang, Jing & Knopf, Herman T. & Hall, Jaclyn M., 2024. "Unveiling spatial mismatch in childcare supply and demand: An excess commuting analysis of home-to-childcare distance in subsidized families," Journal of Transport Geography, Elsevier, vol. 116(C).
    5. Sung, Hyungun, 2023. "Multi-scale moderation impacts of jobs and housing balancing on sustainable commuting behavior in Seoul," Journal of Transport Geography, Elsevier, vol. 110(C).
    6. Chen, Ruoyu & Zhang, Min & Zhou, Jiangping, 2023. "Jobs-housing relationships before and amid COVID-19: An excess-commuting approach," Journal of Transport Geography, Elsevier, vol. 106(C).
    7. See, Justin & Cuaton, Ginbert Permejo & Placino, Pryor & Vunibola, Suliasi & Thi, Huong Do & Dombroski, Kelly & McKinnon, Katharine, 2024. "From absences to emergences: Foregrounding traditional and Indigenous climate change adaptation knowledges and practices from Fiji, Vietnam and the Philippines," World Development, Elsevier, vol. 176(C).

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