Criteria for selecting model updating methods for better temporal transferability
Criteria for selecting model updating methods for better temporal transferability
复制标题
选择模型更新方法以实现更好的时间可迁移性的标准
DOI：
10.1080/23249935.2020.1746862
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Sanko Nobuhiro
中科院分区：
文献类型：
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作者：
Sanko;N.;水谷淳;正司健一・Song Yeon-Jung;Sanko Nobuhiro
当较旧的数据集和较新的数据集分别具有大量和少量的观测值时，离散选择建模者必须决定是使用两个数据集进行模型更新（传输缩放、联合上下文估计、贝叶斯更新和组合传输估计）还是仅使用较新的数据集。本研究调查数据收集时间点和每个时间点的观察数量不同的情况。利用日本名古屋的数据集，将引导法应用于通勤模式选择模型。提出以下标准：（1）当较新的时间点有大量观测值时，仅使用较新的数据； （2）当较近的时间点的观测数量较少时，根据两个时间点上下文的差异以及较旧时间点的样本量，使用转移缩放或联合上下文估计。
When older and more recent datasets have large and small numbers of observations, respectively, then discrete choice modellers must decide whether to utilise both datasets with model updating (transfer scaling, joint context estimation, Bayesian updating, and combined transfer estimation) or only the more recent dataset. This study investigates the case when the data collection time points and the number of observations from each time point differ. Bootstrapping was applied to commuting mode choice models utilising datasets from Nagoya, Japan. The following criteria are proposed: (1) when the more recent time point has a large number of observations, use only the more recent data; (2) when the more recent time point has a smaller number of observations, use transfer scaling or joint context estimation based on the differences in the contexts of the two time points and the sample size from the older time point.