Modeling individuals’ travel decision making in terms of choosing transport modes, route and departure time for daily activities is an indispensable component for transport system optimization and management. Conventional approaches of modeling travel decision making suffer from presumed model structures and parametric specifications. Emerging machine learning algorithms offer data-driven and non-parametric solutions for modeling travel decision making but encounter extrapolation issues (i.e., disability to predict scenarios beyond training samples) due to neglecting behavioral mechanisms in the framework. This study proposes an extrapolation-enhanced approach for modeling travel decision making, leveraging the complementary merits of ensemble machine learning algorithms (Random Forest in our study) and knowledge-based decision-making theory to enhance both predictive accuracy and model extrapolation. The proposed approach is examined using three datasets about travel decision making, including one estimation dataset (for cross-validation) and two test datasets (for model extrapolation tests). Especially, we use two test datasets containing extrapolated choice scenarios with features that exceed the ranges of training samples, to examine the predictive ability of proposed models in extrapolated choice scenarios, which have hardly been investigated by relevant literature. The results show that both proposed models and the direct application of Random Forest (RF) can give quite good predictive accuracy (around 80%) in the estimation dataset. However, RF has a deficient predictive ability in two test datasets with extrapolated choice scenarios. In contrast, the proposed models provide substantially superior predictive performances in the two test datasets, indicating much stronger extrapolation capacity. The model based on the proposed framework could improve the precision score by 274.93% than the direct application of RF in the first test dataset and by 21.9% in the second test dataset. The results indicate the merits of the proposed approach in terms of prediction power and extrapolation ability as compared to existing methods.

在选择日常活动的运输方式，路线和出发时间方面对个人的旅行决策建模，是运输系统优化和管理必不可少的组成部分。对旅行决策进行建模的常规方法受假定的模型结构和参数规范的影响。新兴的机器学习算法为建模旅行决策提供了数据驱动的非参数解决方案，但由于忽略了框架中的行为机制，因此会遇到外推问题（即无法预测超出训练样本的情况）。这项研究提出了一种外推增强的方法，用于对旅行决策进行建模，利用集成机器学习算法（在我们的研究中为Random Forest）和基于知识的决策理论的互补优势来提高预测准确性和模型外推。使用关于旅行决策的三个数据集检查了提出的方法，其中包括一个估计数据集（用于交叉验证）和两个测试数据集（用于模型外推测试）。特别是，我们使用两个测试数据集（其特征超出了训练样本的范围）包含推断出的选择方案，以检验建议模型在推断出的选择方案中的预测能力，相关文献对此几乎没有进行调查。结果表明，所提出的模型和随机森林（RF）的直接应用都可以在估计数据集中提供相当好的预测精度（大约80％）。但是，在具有外推选择方案的两个测试数据集中，RF的预测能力不足。相比之下，所提出的模型在两个测试数据集中提供了显着优越的预测性能，表明更强的外推能力。与第一个测试数据集直接应用RF相比，在第二个测试数据集中使用RF的直接应用提高了274.93％的精度得分，在第二个测试数据集中则提高了21.9％。结果表明，与现有方法相比，该方法在预测能力和外推能力方面具有优势。表示外推能力更强。与第一个测试数据集直接应用RF相比，在第二个测试数据集中使用RF的直接应用提高了274.93％的精度得分，而在第二个测试数据集中则提高了21.9％的精度得分。结果表明，与现有方法相比，该方法在预测能力和外推能力方面具有优势。表示外推能力更强。与第一个测试数据集直接应用RF相比，在第二个测试数据集中使用RF的直接应用提高了274.93％的精度得分，而在第二个测试数据集中则提高了21.9％的精度得分。结果表明，与现有方法相比，该方法在预测能力和外推能力方面具有优势。