Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning
Abstract
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.
Cite
@article{arxiv.1809.05781,
title = {Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning},
author = {Melvin Wong and Bilal Farooq},
journal= {arXiv preprint arXiv:1809.05781},
year = {2018}
}
Comments
Published in the proceedings of IEEE Intelligent Transportation Systems Conference 2018