  Logit models are usually applied when studying individual travel behavior,
i.e., to predict travel mode choice and to gain behavioral insights on traveler
preferences. Recently, some studies have applied machine learning to model
travel mode choice and reported higher out-of-sample predictive accuracy than
traditional logit models (e.g., multinomial logit). However, little research
focuses on comparing the interpretability of machine learning with logit
models. In other words, how to draw behavioral insights from the
high-performance "black-box" machine-learning models remains largely unsolved
in the field of travel behavior modeling.
  This paper aims at providing a comprehensive comparison between the two
approaches by examining the key similarities and differences in model
development, evaluation, and behavioral interpretation between logit and
machine-learning models for travel mode choice modeling. To complement the
theoretical discussions, the paper also empirically evaluates the two
approaches on the stated-preference survey data for a new type of transit
system integrating high-frequency fixed-route services and ridesourcing. The
results show that machine learning can produce significantly higher predictive
accuracy than logit models. Moreover, machine learning and logit models largely
agree on many aspects of behavioral interpretations. In addition, machine
learning can automatically capture the nonlinear relationship between the input
features and choice outcomes. The paper concludes that there is great potential
in merging ideas from machine learning and conventional statistical methods to
develop refined models for travel behavior research and suggests some new
research directions.
