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Machine Learning in Mode Choice Prediction as Part of MPOs’ Regional Travel Demand Models: Is It Time for Change?

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  • Hannaneh Abdollahzadeh Kalantari

    (Department of City and Metropolitan Planning, College of Architecture + Planning, University of Utah, 375S 1530E, Salt Lake City, UT 84112, USA)

  • Sadegh Sabouri

    (Department of Urban Studies and Planning, Massachusetts Institute of Technology (MIT), MIT 9-216, 77 Massachusetts Avenue, Cambridge, MA 02139, USA)

  • Simon Brewer

    (Department of Geography, University of Utah, 375S 1530E, Salt Lake City, UT 84112, USA)

  • Reid Ewing

    (Department of City and Metropolitan Planning, College of Architecture + Planning, University of Utah, 375S 1530E, Salt Lake City, UT 84112, USA)

  • Guang Tian

    (Department of Planning and Urban Studies, University of New Orleans, 378 Milneburg Hall, 2000 Lakeshore Drive, New Orleans, LA 70148, USA)

Abstract

This study aims to improve the predictive accuracy of metropolitan planning organizations’ (MPOs’) travel demand models (TDM) by unraveling the factors influencing transportation mode choices. By exploring the interplay between trip characteristics, socioeconomics, built environment features, and regional conditions, we aim to address existing gaps in MPOs’ TDMs which revolve around the need to also integrate non-motorized modes and a more comprehensive array of features. Additionally, our objective is to develop a more robust predictive model compared to the current nested logit (NL) and multinomial logit (MNL) models commonly employed by MPOs. We apply a one-vs-rest random forest (RF) model to predict mode choices (Home-based-Work, Home-Based-Other, and non-home-based) for over 800,000 trips by 80,000 households across 29 US regions. Validation results demonstrate the RF model’s superior performance compared to conventional NL/MNL models. Key findings highlight that increased travel time and distance are associated with more auto trips, while household vehicle ownership significantly affects car and transit choices. Built environment features, such as activity density, transit density, and intersection density, also play crucial roles in mode preferences. This study offers a more robust predictive framework that can be directly applied in MPO TDMs, contributing to more accurate and inclusive transportation planning.

Suggested Citation

  • Hannaneh Abdollahzadeh Kalantari & Sadegh Sabouri & Simon Brewer & Reid Ewing & Guang Tian, 2025. "Machine Learning in Mode Choice Prediction as Part of MPOs’ Regional Travel Demand Models: Is It Time for Change?," Sustainability, MDPI, vol. 17(8), pages 1-29, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3580-:d:1635691
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    References listed on IDEAS

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    1. Cynthia Chen & Hongmian Gong & Robert Paaswell, 2008. "Role of the built environment on mode choice decisions: additional evidence on the impact of density," Transportation, Springer, vol. 35(3), pages 285-299, May.
    2. Ding, Chuan & Wang, Donggen & Liu, Chao & Zhang, Yi & Yang, Jiawen, 2017. "Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 65-80.
    3. Handy, Susan & Cao, Xinyu & Mokhtarian, Patricia L., 2005. "Correlation or causality between the built environment and travel behavior? Evidence from Northern California," University of California Transportation Center, Working Papers qt5b76c5kg, University of California Transportation Center.
    4. Lawrence Frank & Mark Bradley & Sarah Kavage & James Chapman & T. Lawton, 2008. "Urban form, travel time, and cost relationships with tour complexity and mode choice," Transportation, Springer, vol. 35(1), pages 37-54, January.
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