AI 中文总结
本研究将因果森林应用于全国家庭出行调查数据，量化新冠疫情对出行方式选择的异质性因果效应，发现汽车出行占比上升，短途、高收入家庭及女性群体增幅最大，为交通政策制定提供依据。
AI 中文摘要
研究背景与问题：尽管出行行为的因果分析是一个新兴领域，但通过因果建模估计出行方式选择的异质性仍未得到探索。本研究展示了一种新的因果方法——因果森林（causal forest）的应用，以量化新冠疫情引发的出行方式选择转变中的异质性。方法：我们将非参数因果机器学习方法因果森林应用于来自2017年和2022年全国家庭出行调查（National Household Travel Survey）的802935条出行记录，其中2017年波次作为疫情前的对照组，2022年波次代表处理组。在潜在结果框架内，我们估计了不同社会人口群体和出行特征下的平均处理效应（ATE）、异质性处理效应（HTE）和条件平均处理效应（CATE）。研究结果：我们的结果显示，汽车出行占比的平均处理效应估计值增加了1.86个百分点（pp），而公共交通和步行出行占比分别下降了0.38个百分点和1.57个百分点。汽车使用量增长最大的群体包括短途出行（1英里或以内）、年收入超过20万美元的家庭以及女性出行者。创新性：这是因果森林首次应用于出行方式选择的研究之一，也是首个使用因果机器学习估计疫情对出行方式选择分析的因果效应的研究。实际应用：本研究探讨了交通规划背景下因果森林的方法学优势、固有假设及局限性。该方法应用于新冠疫情出行数据，以说明因果异质性分析如何能更深入地理解出行方式选择的变化，这些见解对规划者和政策制定者制定干预下出行方式转变相关政策具有重要价值。
英文摘要
Objectives: While causal analysis of travel behavior is an emerging field, estimating heterogeneity in mode choice through causal modeling remains unexplored. This study demonstrates the application of a novel causal method, causal forest, to quantify the heterogeneity in travel mode choice shifts caused by the COVID-19 pandemic. Methods: We applied causal forests, a non-parametric causal machine learning method, to 802,935 trip records from the 2017 and 2022 waves of the National Household Travel Survey. The 2017 wave serves as the pre-pandemic control group, while the 2022 wave represents the treatment condition. Within the potential outcomes framework, we estimate average treatment effects (ATE), heterogeneous treatment effects (HTE), and conditional average treatment effects (CATE) across diverse socio-demographic groups and trip characteristics. Findings: Our results reveal an estimated ATE of a 1.86 percentage point (pp) increase in car-mode share, contrasted with decreases of 0.38 pp and 1.57 pp in public transit and walking, respectively. The largest increases in car use appeared for short-distance trips (one mile or less), households with annual incomes exceeding USD 200,000, and female travelers. Novelty: This is one of the first applications of causal forests to travel mode choice, and the first to use causal machine learning to estimate the pandemic's causal effect on mode choice analysis. Practical Applications: This study discusses methodological advantages, inherent assumptions, and limitations of causal forests within the context of transportation planning. This methodology is applied to COVID-19 travel data to illustrate how causal heterogeneity analysis can offer a deeper understanding of changes in mode choice. These insights are valuable for planners and policymakers in making policies related to mode shifts under an intervention.
CommentsSubmitted to the Transportation Research Board 2027 Annual Meeting for presentation. TRB paper number TRBAM-27-04345
