Title: A combined entropy and utility based generative model for large scale multiple discrete-continuous travel behaviour data,
Abstract: Generative models, either by simple clustering algorithms or deep neural
network architecture, have been developed as a probabilistic estimation method
for dimension reduction or to model the underlying properties of data
structures. Although their apparent use has largely been limited to image
recognition and classification, generative machine learning algorithms can be a
powerful tool for travel behaviour research. In this paper, we examine the
generative machine learning approach for analyzing multiple discrete-continuous
(MDC) travel behaviour data to understand the underlying heterogeneity and
correlation, increasing the representational power of such travel behaviour
models. We show that generative models are conceptually similar to choice
selection behaviour process through information entropy and variational
Bayesian inference. Specifically, we consider a restricted Boltzmann machine
(RBM) based algorithm with multiple discrete-continuous layer, formulated as a
variational Bayesian inference optimization problem. We systematically describe
the proposed machine learning algorithm and develop a process of analyzing
travel behaviour data from a generative learning perspective. We show parameter
stability from model analysis and simulation tests on an open dataset with
multiple discrete-continuous dimensions and a size of 293,330 observations. For
interpretability, we derive analytical methods for conditional probabilities as
well as elasticities. Our results indicate that latent variables in generative
models can accurately represent joint distribution consistently w.r.t multiple
discrete-continuous variables. Lastly, we show that our model can generate
statistically similar data distributions for travel forecasting and prediction.