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Intrazonal or interzonal? Improving intrazonal travel forecast in a four-step travel demand model

Author

Listed:
  • Keunhyun Park

    (Utah State University)

  • Sadegh Sabouri

    (University of Utah)

  • Torrey Lyons

    (University of Utah)

  • Guang Tian

    (University of New Orleans)

  • Reid Ewing

    (University of Utah)

Abstract

Conventional four-step travel demand models, used by most metropolitan planning organizations (MPOs), state departments of transportation, and local planning agencies, are the basis for long-range transportation planning in the United States. Trip distribution—whether the trip is intrazonal (internal) or interzonal (external)—is one of the essential steps in travel demand forecasting. However, the current intrazonal forecasts based on a gravity model involve flawed assumptions, primarily due to a lack of considerations on differences in zone size, land use, and street network patterns. In this study, we first survey 25 MPOs about how they model intrazonal travel and find the state of the practice to be dominated by the gravity model. Using travel data from 31 diverse regions in the U.S., we develop an approach to enhance the conventional model by including more built environment D variables and by using multilevel logistic regression. The models’ predictive capability is confirmed using k-fold cross-validation. The study results provide practical implications for state and local planning and transportation agencies with better accuracy and generalizability.

Suggested Citation

  • Keunhyun Park & Sadegh Sabouri & Torrey Lyons & Guang Tian & Reid Ewing, 2020. "Intrazonal or interzonal? Improving intrazonal travel forecast in a four-step travel demand model," Transportation, Springer, vol. 47(5), pages 2087-2108, October.
    • Handle: RePEc:kap:transp:v:47:y:2020:i:5:d:10.1007_s11116-019-10002-0
    DOI: 10.1007/s11116-019-10002-0
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    References listed on IDEAS

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    Cited by:

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    3. Yang, Binyu & Tian, Yuan & Wang, Jian & Hu, Xiaowei & An, Shi, 2022. "How to improve urban transportation planning in big data era? A practice in the study of traffic analysis zone delineation," Transport Policy, Elsevier, vol. 127(C), pages 1-14.

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