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.

中文翻译：

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选择模型更新方法以获得更好的时间可转移性的标准

当较旧和较新的数据集分别具有大量和少量观察值时，离散选择建模者必须决定是将两个数据集都用于模型更新（转移缩放、联合上下文估计、贝叶斯更新和组合转移估计）还是仅使用更多最近的数据集。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。利用来自日本名古屋的数据集，将 Bootstrapping 应用于通勤模式选择模型。提出以下标准：（1）当较近的时间点有大量的观测时，只使用较近的数据；(2) 当较近的时间点有较少数量的观察时，