This paper proposes a novel combination of machine learning techniques and discrete choice models for route choice modeling. The data-driven choice set generation method identifies routes characteristics by clustering, and implicitly generates the choice set by sampling route characteristic attributes from the clusters. Important features are selected by random forests for route choice model development. With the selected features, the methodological-iterative approach is applied to specify the utility functions and to find significant explanatory variables automatically.

Results show that the proposed data-driven method produces a discrete route choice model not only with strong explanatory power, but also with high prediction accuracy compared to models estimated with conventional choice set generation methods.

本文提出了一种机器学习技术和离散选择模型的新颖组合，用于路线选择建模。数据驱动选择集生成方法通过聚类来识别路由特征，并通过从聚类中采样路由特征属性来隐式生成选择集。随机森林选择重要特征以进行路线选择模型开发。利用选定的功能，该方法论迭代方法可用于指定效用函数并自动查找重要的解释变量。

结果表明，与传统选择集生成方法估计的模型相比，所提出的数据驱动方法不仅具有强大的解释能力，而且具有较高的预测精度，可产生离散的路线选择模型。