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.

通过研究货运方式选择模型中机器学习分类器的实用性，本研究调查了八种常用的机器学习分类器，包括朴素贝叶斯、支持向量机、人工神经网络、K 最近邻、分类与回归树、随机森林、提升 (boosting) 和装袋 (bagging)，并与经典的多项式逻辑模型进行比较。使用美国 2012 年商品流量调查数据作为主要数据源，并且通过次要数据源补充了空间属性。根据预测准确度结果对这些分类器的性能进行比较。当前研究还考察了样本大小和训练 - 测试数据分割比对各种方法的预测能力的影响。此外，估计了变量的重要性以确定变量对货运方式选择的影响。结果表明，基于树的集成分类器效果最好。具体而言，随机森林产生了最准确的预测，紧随其后的是提升和装袋。在变量的重要性方面，货运特征，如运输距离、发货人的行业分类和货运规模，是货运方式选择决策中最重要的因素。