  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.
