标题： 深度神经网络用于选择分析：统计学习理论视角 显示英文标题

标题： Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective

摘要： 虽然研究人员越来越多地使用深度神经网络（DNN）来分析个体选择，但过拟合和可解释性问题在理论和实践中仍然是障碍。 通过使用统计学习理论，本研究提出一个框架来检验估计误差与近似误差之间，以及预测损失与解释损失之间的权衡。 它通过将解释损失的度量形式化为真实和估计的选择概率函数之间的差异，来实现选择分析中的DNN可解释性。 本研究还使用统计学习理论来上限DNN中预测和解释损失的估计误差，揭示了为什么DNN没有过拟合问题。 然后模拟了三种场景，将DNN与二元逻辑模型（BNL）进行比较。 我们发现，在大多数场景中，DNN在预测和解释方面都优于BNL，更大的样本量释放了DNN的预测能力，但并未释放BNL的预测能力。 DNN还被用于基于国家家庭出行调查2017（NHTS2017）数据集分析出行目的和出行方式的选择。 这些实验表明，DNN可以用于超出当前需求预测实践的选择分析，因为它具有内在效用解释、容纳各种信息格式的灵活性以及自动学习效用规范的能力。 除非建模者对选择任务有完全的了解，并且样本量较小，否则DNN在预测和可解释性方面都优于BNL。 总体而言，统计学习理论可以成为未来在非渐近数据制度或在选择分析中使用高维统计模型的研究基础，实验展示了DNN在政策和行为分析中的广泛应用的可行性和有效性。 显示英文摘要

摘要： While researchers increasingly use deep neural networks (DNN) to analyze individual choices, overfitting and interpretability issues remain as obstacles in theory and practice. By using statistical learning theory, this study presents a framework to examine the tradeoff between estimation and approximation errors, and between prediction and interpretation losses. It operationalizes the DNN interpretability in the choice analysis by formulating the metrics of interpretation loss as the difference between true and estimated choice probability functions. This study also uses the statistical learning theory to upper bound the estimation error of both prediction and interpretation losses in DNN, shedding light on why DNN does not have the overfitting issue. Three scenarios are then simulated to compare DNN to binary logit model (BNL). We found that DNN outperforms BNL in terms of both prediction and interpretation for most of the scenarios, and larger sample size unleashes the predictive power of DNN but not BNL. DNN is also used to analyze the choice of trip purposes and travel modes based on the National Household Travel Survey 2017 (NHTS2017) dataset. These experiments indicate that DNN can be used for choice analysis beyond the current practice of demand forecasting because it has the inherent utility interpretation, the flexibility of accommodating various information formats, and the power of automatically learning utility specification. DNN is both more predictive and interpretable than BNL unless the modelers have complete knowledge about the choice task, and the sample size is small. Overall, statistical learning theory can be a foundation for future studies in the non-asymptotic data regime or using high-dimensional statistical models in choice analysis, and the experiments show the feasibility and effectiveness of DNN for its wide applications to policy and behavioral analysis.