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Introducing synthetic pseudo panels: application to transport behaviour dynamics
Transportation ( IF 4.3 ) Pub Date : 2020-09-12 , DOI: 10.1007/s11116-020-10137-5
Stanislav S. Borysov , Jeppe Rich

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.

中文翻译:

引入合成伪面板:应用于运输行为动力学

在本文中,提出了一种通过从重复的横截面数据构建详细的合成伪面板来研究旅行行为动力学的方法。该方法基于使用条件变分自编码器 (CVAE) 对以详细的社会经济概况为条件的旅行偏好的高维联合分布进行建模。CVAE 是一种基于神经网络的生成模型,它允许对非常详细的联合和条件分布进行建模,这些分布可能由数十个甚至数百个属性以灵活的非参数形式定义。所提出的方法用于根据他们的旅行行为随时间变化的速度将详细的个人群组分为慢行者和快行者。这提供了一个有趣的洞察力,可以很容易地改变自己的行为,而不是那些不太灵活的人。具体而言,我们从涵盖 2006 年至 2016 年期间的大型丹麦横截面数据集中研究了固定伪面板个人的交通偏好动态。 2006 年和 2016 年的旅行偏好分布的比较表明,原型快速移动者是一个住在大城市的单身年轻女性,而典型的慢行者是一个住在城外独立屋的核心家庭高收入中年男子。然而,鉴于可以在非常详细的社会经济分类中对个人进行排名,因此可以探索许多其他关系。最后,
更新日期:2020-09-12
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