  This study proposes a novel approach that combines theory and data-driven
choice models using Artificial Neural Networks (ANNs). In particular, we use
continuous vector representations, called embeddings, for encoding categorical
or discrete explanatory variables with a special focus on interpretability and
model transparency. Although embedding representations within the logit
framework have been conceptualized by Pereira (2019), their dimensions do not
have an absolute definitive meaning, hence offering limited behavioral insights
in this earlier work. The novelty of our work lies in enforcing
interpretability to the embedding vectors by formally associating each of their
dimensions to a choice alternative. Thus, our approach brings benefits much
beyond a simple parsimonious representation improvement over dummy encoding, as
it provides behaviorally meaningful outputs that can be used in travel demand
analysis and policy decisions. Additionally, in contrast to previously
suggested ANN-based Discrete Choice Models (DCMs) that either sacrifice
interpretability for performance or are only partially interpretable, our
models preserve interpretability of the utility coefficients for all the input
variables despite being based on ANN principles. The proposed models were
tested on two real world datasets and evaluated against benchmark and baseline
models that use dummy-encoding. The results of the experiments indicate that
our models deliver state-of-the-art predictive performance, outperforming
existing ANN-based models while drastically reducing the number of required
network parameters.
