  Machine learning has proved to be very successful for making predictions in
travel behavior modeling. However, most machine-learning models have complex
model structures and offer little or no explanation as to how they arrive at
these predictions. Interpretations about travel behavior models are essential
for decision makers to understand travelers' preferences and plan policy
interventions accordingly. Therefore, this paper proposes to apply and extend
the model distillation approach, a model-agnostic machine-learning
interpretation method, to explain how a black-box travel mode choice model
makes predictions for the entire population and subpopulations of interest.
Model distillation aims at compressing knowledge from a complex model (teacher)
into an understandable and interpretable model (student). In particular, the
paper integrates model distillation with market segmentation to generate more
insights by accounting for heterogeneity. Furthermore, the paper provides a
comprehensive comparison of student models with the benchmark model (decision
tree) and the teacher model (gradient boosting trees) to quantify the fidelity
and accuracy of the students' interpretations.
