Modeling Freight Mode Choice Using Machine Learning Classifiers: A
  Comparative Study Using the Commodity Flow Survey (CFS) Data
Majbah Uddin Sabreena Anowar Naveen Eluru
This study explores the usefulness of machine learning classifiers for
modeling freight mode choice. We investigate eight commonly used machine
learning classifiers, namely Naive Bayes, Support Vector Machine, Artificial
Neural Network, K-Nearest Neighbors, Classification and Regression Tree, Random
Forest, Boosting and Bagging, along with the classical Multinomial Logit model.
US 2012 Commodity Flow Survey data are used as the primary data source; we
augment it with spatial attributes from secondary data sources. The performance
of the classifiers is compared based on prediction accuracy results. The
current research also examines the role of sample size and training-testing
data split ratios on the predictive ability of the various approaches. In
addition, the importance of variables is estimated to determine how the
variables influence freight mode choice. The results show that the tree-based
ensemble classifiers perform the best. Specifically, Random Forest produces the
most accurate predictions, closely followed by Boosting and Bagging. With
regard to variable importance, shipment characteristics, such as shipment
distance, industry classification of the shipper and shipment size, are the
most significant factors for freight mode choice decisions.