This study compares the performance of a causal and a predictive model in modeling travel mode choice in three neighborhoods in Chicago. A causal discovery algorithm and a causal inference technique were used to extract the causal relationships in the mode choice decision making process and to estimate the quantitative causal effects between the variables both directly from observational data. The model results reveal that trip distance and vehicle ownership are the direct causes of mode choice in the three neighborhoods. Artificial neural network models were estimated to predict mode choice. Their accuracy was over 70%, and the SHAP values obtained measure the importance of each variable. We find that both the causal and predictive modeling approaches are useful for the purpose they serve. We also note that the study of mode choice behavior through causal modeling is mostly unexplored, yet it could transform our understanding of the mode choice behavior. Further research is needed to realize the full potential of these techniques in modeling mode choice.

翻译：本研究比较了因果模型与预测模型在芝加哥三个社区出行方式选择建模中的表现。通过使用因果发现算法和因果推断技术，直接从观测数据中提取了出行方式选择决策过程中的因果关系，并估算了变量间的量化因果效应。模型结果表明，出行距离和车辆拥有量是这三个社区出行方式选择的直接原因。研究还构建了人工神经网络模型用于预测出行方式选择，其准确率超过70%，并通过SHAP值测量了各变量的重要性。研究发现，因果模型和预测模型在各自目标下均具有实用价值。同时指出，通过因果建模研究出行方式选择行为尚未得到充分探索，但这种研究范式有可能转变我们对出行方式选择行为的理解。未来需进一步研究以充分发掘这些技术在出行方式选择建模中的潜力。