This article presents one of the pioneering studies on causal modeling in travel mode choice decision-making using causal discovery algorithms. These models are a major advancement from conventional correlation-based techniques. We propose a novel methodology that combines causal discovery with structural equation modeling (SEM). This modeling approach overcomes some of the limitations of SEM by combining the strengths of both causal discovery and SEM. Causal discovery algorithms determine causal graphs from observational data and domain knowledge, and SEMs estimate direct causal effects and test the performance of causal discovery algorithms. In this study, we test four causal discovery algorithms: Peter-Clark (PC), Fast Causal Inference (FCI), Fast Greedy Equivalence Search (FGES), and Direct Linear Non-Gaussian Acyclic Models (DirectLiNGAM). The results show that DirectLiNGAM based SEM model best captures causality in mode choice behavior. It passes several goodness-of-fit tests, including Root Mean Square Error of Approximation (RMSEA) and Goodness-of-Fit Index (GFI), and it achieves the lowest Bayesian Information Criterion (BIC) value. The analyses are conducted on data collected from the 2017 National Household Travel Survey in the New York Metropolitan area.

翻译：本文是运用因果发现算法对出行方式选择决策进行因果建模的开创性研究之一。此类模型相较于传统的基于相关性分析方法取得了重大进展。我们提出了一种新颖的方法论，将因果发现与结构方程模型相结合。这种建模方法通过综合因果发现与结构方程模型的优势，克服了结构方程模型的部分局限性。因果发现算法利用观测数据和领域知识确定因果图，而结构方程模型则用于估计直接因果效应并检验因果发现算法的性能。本研究测试了四种因果发现算法：Peter-Clark (PC)、快速因果推断 (FCI)、快速贪婪等价搜索 (FGES) 和直接线性非高斯无环模型 (DirectLiNGAM)。结果表明，基于DirectLiNGAM的结构方程模型最能捕捉出行方式选择行为中的因果关系。该模型通过了多项拟合优度检验，包括近似均方根误差 (RMSEA) 和拟合优度指数 (GFI)，并取得了最低的贝叶斯信息准则 (BIC) 值。分析基于2017年纽约大都会区全美家庭出行调查数据展开。