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

Hannaneh Abdollahzadeh Kalantari (), Sadegh Sabouri, Simon Brewer, Reid Ewing and Guang Tian
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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

Sustainability, 2025, vol. 17, issue 8, 1-29

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.

Keywords: MPOs’ travel demand models; mode choice prediction; built environment; machine learning; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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