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Updated: Apr 3, 2026

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
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Modeling Freight Vehicle Type Choice using Machine Learning and Discrete Choice Methods.

Usman Ahmed1, Matthew J Roorda1

  • 1Department of Civil & Mineral Engineering, University of Toronto, Toronto, Canada.

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Summary
This summary is machine-generated.

Firms choose vehicle types for shipments using complex logistics decisions. A random forest model accurately predicts choices, outperforming other methods by up to 9.6%.

Keywords:
discrete choice modelmachine learningrandom foresturban freight transportationvehicle type choice

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Area of Science:

  • Logistics and Supply Chain Management
  • Transportation Science
  • Machine Learning Applications

Background:

  • Vehicle type selection is a critical logistics decision for firms.
  • The process is complex due to multiple interacting agents and limited transportation data.
  • Understanding these choices is vital for efficient freight operations.

Purpose of the Study:

  • To model and predict road transport vehicle type choices for outbound shipments.
  • To apply a random forest machine learning algorithm to capture complex interactions.
  • To compare the performance of the random forest model against traditional logit models.

Main Methods:

  • Utilized data from Commercial Travel Surveys on outbound shipment transportation.
  • Employed a random forest machine learning algorithm for choice modeling.
  • Calculated SHAP-based variable importance to identify key decision factors.

Main Results:

  • Firm employment and shipment weight were identified as the most significant variables influencing vehicle type choice.
  • The random forest model demonstrated superior predictive accuracy compared to multinomial and mixed logit models.
  • Accuracy improvements were 7.8% over multinomial logit and 9.6% over mixed logit models.

Conclusions:

  • Random forest models offer a more accurate approach to predicting vehicle type choice in logistics.
  • Machine learning provides valuable insights into complex transportation decision-making processes.
  • The findings can aid firms in optimizing vehicle selection for outbound shipments.