Introducing synthetic pseudo panels: application to transport behaviour dynamics

Stanislav Borysov*, Jeppe Rich

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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In this paper, a method to study travel behaviour dynamics by constructing detailed synthetic pseudo panels from repeated cross-sectional data is presented. The method is based on the modelling of a high-dimensional joint distribution of travel preferences conditional on detailed socio-economic profiles by using a conditional variational autoencoder (CVAE). The CVAE is a neural-network-based generative model which allows the modelling of very detailed joint and conditional distributions, potentially defined by dozens or even hundreds of attributes in a flexible non-parametric form. The proposed method is used to rank detailed cohorts of individuals into slow and fast movers with respect to the speed at which their travel behaviour change over time. This gives an interesting insight into the types of individuals who are easily motivated to change their behaviour as opposed to those who are less flexible. Specifically, we investigate the dynamics of transport preferences for a fixed pseudo panel of individuals from a large Danish cross-sectional data set covering the period from 2006 to 2016. The comparison of the travel preference distributions from 2006 and 2016 shows that the prototypical fast mover is a single young woman who lives in a large city, whereas the typical slow mover is a middle-aged man with high income from a nuclear family who lives in a detached house outside a city. However, given that it is possible to rank individuals across very detailed socio-economic classifications, many other relationships can be explored. Finally, the CVAE can be directly applied to the population synthesis problem in microsimulation by modelling the distribution of socio-economic profiles conditional on other variables.
Original languageEnglish
Number of pages22
Publication statusAccepted/In press - 2021

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