ABSTRACT

This study explores the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naïve 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.

摘要

本研究探讨了机器学习分类器对货运模式选择建模的有用性。我们研究了八种常用的机器学习分类器，即朴素贝叶斯、支持向量机、人工神经网络、K-最近邻、分类和回归树、随机森林、Boosting 和 Bagging，以及经典的多项 Logit 模型。以美国2012年商品流量调查数据为主要数据来源；我们用来自辅助数据源的空间属性来增强它。根据预测精度结果比较分类器的性能。当前的研究还检查了样本大小和训练测试数据分割率对各种方法的预测能力的作用。此外，估计变量的重要性以确定变量如何影响货运模式选择。结果表明，基于树的集成分类器表现最好。具体来说，随机森林产生最准确的预测，紧随其后的是 Boosting 和 Bagging。就可变重要性而言，货运特征，例如货运距离、托运人的行业分类和货运规模，是货运模式选择决策的最重要因素。