  We present a Gaussian Process - Latent Class Choice Model (GP-LCCM) to
integrate a non-parametric class of probabilistic machine learning within
discrete choice models (DCMs). Gaussian Processes (GPs) are kernel-based
algorithms that incorporate expert knowledge by assuming priors over latent
functions rather than priors over parameters, which makes them more flexible in
addressing nonlinear problems. By integrating a Gaussian Process within a LCCM
structure, we aim at improving discrete representations of unobserved
heterogeneity. The proposed model would assign individuals probabilistically to
behaviorally homogeneous clusters (latent classes) using GPs and simultaneously
estimate class-specific choice models by relying on random utility models.
Furthermore, we derive and implement an Expectation-Maximization (EM) algorithm
to jointly estimate/infer the hyperparameters of the GP kernel function and the
class-specific choice parameters by relying on a Laplace approximation and
gradient-based numerical optimization methods, respectively. The model is
tested on two different mode choice applications and compared against different
LCCM benchmarks. Results show that GP-LCCM allows for a more complex and
flexible representation of heterogeneity and improves both in-sample fit and
out-of-sample predictive power. Moreover, behavioral and economic
interpretability is maintained at the class-specific choice model level while
local interpretation of the latent classes can still be achieved, although the
non-parametric characteristic of GPs lessens the transparency of the model.
