引入超级伪面板：应用于运输偏好动力学
Introducing Super Pseudo Panels: Application to Transport Preference Dynamics
摘要：
我们提出了一种新的方法来构建合成的伪面板数据的横截面数据。伪面板和它打算描述的偏好是在个人层面上构建的，不受跨队列的聚合偏差的影响。这是通过创建整个数据集的高维概率模型表示来实现的，这允许以保留原始数据的所有固有相关性的方式从概率模型中进行采样。关键是使用基于条件变分自动编码器（CVAE）框架的深度学习算法。从建模的角度来看，一个基于模型的research的概念创造了一些机会，数据可以被组织和构建，以满足非常具体的需求，其中异构伪面板的形成代表一个。在这方面，优点是能够用严重的聚合偏差（当聚合成群组时）来换取非系统性的噪声干扰。此外，该方法可以探索高维稀疏偏好分布及其与个体特定特征的联系，这是不可能的，如果应用传统的伪面板方法。我们使用所提出的方法来揭示一个固定的伪面板的个人的基础上，涵盖了从2006年到2016年期间的大型丹麦横截面数据集的交通偏好的动态。该模型还被用来分类个人到“慢”和“快”的移动者相对于他们的偏好随着时间的推移而变化的速度。研究发现，典型的快速移动是一个年轻的女人谁作为一个单身生活在一个大城市，而典型的缓慢移动是一个中年男子与高收入的核心家庭谁住在一个独立的房子外的城市。
英文摘要：
We propose a new approach for constructing synthetic pseudo-panel data from cross-sectional data. The pseudo panel and the preferences it intends to describe is constructed at the individual level and is not affected by aggregation bias across cohorts. 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 deep learning algorithms based on the Conditional Variational Autoencoder (CVAE) framework. From a modelling perspective, the concept of a model-based resampling creates a number of opportunities in that data can be organized and constructed to serve very specific needs of which the forming of heterogeneous pseudo panels represents one. The advantage, in that respect, is the ability to trade a serious aggregation bias (when aggregating into cohorts) for an unsystematic noise disturbance. Moreover, the approach makes it possible to explore high-dimensional sparse preference distributions and their linkage to individual specific characteristics, which is not possible if applying traditional pseudo-panel methods. 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 covering the period from 2006 to 2016. The model is also utilized to classify individuals into 'slow' and 'fast' movers with respect to the speed at which their preferences change over time. It is found that the prototypical fast mover is a young woman who lives as a single 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
