Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning
This work addresses travel behavior analysis for transportation researchers, but it is incremental as it applies an existing generative method to a new domain-specific dataset.
The paper tackled the problem of identifying latent characteristics in travel decision making by introducing a generative modelling framework based on a Restricted Boltzmann Machine, applied to mode choice survey data from Quebec, Canada. Results showed a significant impact on model likelihood statistics, indicating the suitability of machine learning for modelling complex behavior interactions.
In this paper, we implement an information-theoretic approach to travel behaviour analysis by introducing a generative modelling framework to identify informative latent characteristics in travel decision making. It involves developing a joint tri-partite Bayesian graphical network model using a Restricted Boltzmann Machine (RBM) generative modelling framework. We apply this framework on a mode choice survey data to identify abstract latent variables and compare the performance with a traditional latent variable model with specific latent preferences -- safety, comfort, and environmental. Data collected from a joint stated and revealed preference mode choice survey in Quebec, Canada were used to calibrate the RBM model. Results show that a signficant impact on model likelihood statistics and suggests that machine learning tools are highly suitable for modelling complex networks of conditional independent behaviour interactions.