  This paper introduces the concept of travel behavior embeddings, a method for
re-representing discrete variables that are typically used in travel demand
modeling, such as mode, trip purpose, education level, family type or
occupation. This re-representation process essentially maps those variables
into a latent space called the \emph{embedding space}. The benefit of this is
that such spaces allow for richer nuances than the typical transformations used
in categorical variables (e.g. dummy encoding, contrasted encoding, principal
components analysis). While the usage of latent variable representations is not
new per se in travel demand modeling, the idea presented here brings several
innovations: it is an entirely data driven algorithm; it is informative and
consistent, since the latent space can be visualized and interpreted based on
distances between different categories; it preserves interpretability of
coefficients, despite being based on Neural Network principles; and it is
transferrable, in that embeddings learned from one dataset can be reused for
other ones, as long as travel behavior keeps consistent between the datasets.
  The idea is strongly inspired on natural language processing techniques,
namely the word2vec algorithm. Such algorithm is behind recent developments
such as in automatic translation or next word prediction. Our method is
demonstrated using a model choice model, and shows improvements of up to 60\%
with respect to initial likelihood, and up to 20% with respect to likelihood of
the corresponding traditional model (i.e. using dummy variables) in
out-of-sample evaluation. We provide a new Python package, called PyTre (PYthon
TRavel Embeddings), that others can straightforwardly use to replicate our
results or improve their own models. Our experiments are themselves based on an
open dataset (swissmetro).
