Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning

Melvin Wong; Bilal Farooq

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arXiv · 1809.05781

Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning

Melvin Wong·Bilal Farooq

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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.

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Melvin Wong, Bilal Farooq. 2018-09-15. Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning. https://arxiv.org/abs/1809.05781

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