Identifying “slow” and “fast” movers in a travel preference space: Application of a synthetic pseudo-panel approach

Abstract

We present a new approach to studying travel preference dynamics based on constructing a synthetic pseudo-panel (SPP) from repeated cross-sectional data. This is accomplished by creating a high-dimensional probabilistic model representation of the entire data set, which allows sampling from the probabilistic model in such a way that all of the intrinsic correlation properties of the original data are preserved. The key to this is the use of novel deep learning algorithms based on the Conditional Variational Autoencoder (CVAE) framework. We use the presented approach to reveal the dynamics of transport preferences for a fixed pseudo-panel of individuals based on a large Danish cross-sectional data set from 2006 to 2016. The model is utilized to classify individuals into 'slow' and 'fast' movers with respect to the speed of which their preferences change over time. It is found that the prototypical fast mover is a young woman who lives as single in a large city whereas the typical slow mover is a middle-aged man with high income from a nuclear family that lives in a detached house outside a city.