Introducing synthetic pseudo panels: application to transport behaviour dynamics
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Introducing synthetic pseudo panels: application to transport behaviour dynamics Stanislav S. Borysov1 · Jeppe Rich1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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 socioeconomic profiles conditional on other variables. Keywords Dynamics of behaviour · Pseudo-panel analysis · Conditional variational autoencoder · Deep learning · Generative modelling · Population synthesis
* Stanislav S. Borysov [email protected] Jeppe Rich [email protected] 1
Department of Technology, Management and Economics, Technical University of Denmark (DTU), 2800 Kgs. Lyngby, Denmark
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Vol.:(0123456789)
Transportation
Introduction Understanding behavioural preferences and their dynamics, whether these are related to transport or any other domain, is a fundamental research question which affects not only models and predictions but also the way policies are designed and towards whom they should be targeted. Such investigations not only can pinpoint windows of opportunities for specific groups of people which can be more easily motivated to change their behaviour but also identify people in life-course periods where such changes are difficult. It is beyond doubt that habits and preferences change over time (Gärling and Axhausen 2003) and affect transport behaviour in significant ways. Examples include Vij et al. (2017) who consider modal preference shifts
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