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 significant impact on model likelihood statistics and suggests that machine learning tools are highly suitable for modelling complex networks of conditional independent behaviour interactions.
Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning
2018-11-01
474479 byte
Conference paper
Electronic Resource
English
Normative beliefs and modality styles: a latent class and latent variable model of travel behaviour
Online Contents | 2016
|Normative beliefs and modality styles: a latent class and latent variable model of travel behaviour
Online Contents | 2016
|Methodological Developments in Travel Behaviour Modelling
British Library Conference Proceedings | 2001
|