论文标题
在旅行模式选择中确定因果关系
Determining Causality in Travel Mode Choice
论文作者
论文摘要
本文介绍了使用因果发现算法在旅行模式选择决策中进行因果建模的开创性研究之一。这些模型是传统基于相关技术的重大进步。我们提出了一种新颖的方法，将因果发现与结构方程建模（SEM）相结合。这种建模方法通过结合因果发现和SEM的优势来克服SEM的某些局限性。因果发现算法从观察数据和域知识中确定因果图，SEMS估计直接因果效应并测试因果发现算法的性能。在这项研究中，我们测试了四种因果发现算法：Peter-Clark（PC），快速因果推理（FCI），快速贪婪的等效搜索（FGES）和直接线性的非高斯无循环模型（Directlingam）。结果表明，基于Directlingam的SEM模型最佳捕获模式选择行为中的因果关系。它通过了几个拟合优点测试，包括近似均方根误差（RMSEA）和拟合索引（GFI），并达到了最低的贝叶斯信息标准（BIC）值。这些分析是根据纽约大都会地区2017年全国家庭旅行调查收集的数据进行的。
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.
