Accommodating correlation across days in multiple discrete-continuous models for time use
Accommodating correlation across days in multiple discrete-continuous models for time use
复制标题
在多个离散-连续模型中适应时间使用的跨天相关性
DOI：
10.1080/21680566.2020.1721379
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
A. Daly
中科院分区：
文献类型：
--
作者：
C. Calastri;S. Hess;A. Pinjari;A. Daly
MDCEV 建模框架已成为时间分配建模的首选方法，数据通常通过旅行或活动日记收集。然而，标准实现未能认识到这样一个事实，即许多数据集包含同一个人的多天信息，并且不同天之间可能存在相关性和替换。本文讨论了这些影响的理论调节为何并不简单，特别是在日和多日级别的预算限制下。我们依靠加性效用函数，使用具有多元随机分布的混合 MDCEV 模型来调节日内和日间活动之间的相关性。我们使用众所周知的时间使用数据集来说明我们的方法，证实了我们的理论观点，并强调了在模型拟合和行为洞察方面允许跨天关联的好处。
The MDCEV modelling framework has established itself as the preferred method for modelling time allocation, with data very often collected through travel or activity diaries. However, standard implementations fail to recognise the fact that many of these datasets contain information on multiple days for the same individual, with possible correlations and substitution between days. This paper discusses how the theoretical accommodation of these effects is not straightforward, especially with budget constraints at the day and multi-day level. We rely on additive utility functions where we accommodate correlation between activities at the within-day and between-day level using a mixed MDCEV model, with multivariate random distributions. We illustrate our approach using a well-known time use datasets, confirming our theoretical points and highlighting the benefits of allowing for correlation across days in terms of model fit and behavioural insights.