Recently, people are increasingly interested in understanding broader and longer-term individual activity-travel behaviors across days or even weeks. However, the relationship of individual daily activity-travel behaviors over multi-days only remains partially revealed, especially between workdays and day-offs. Thus, we develop an effective framework to extract individual daily activity-travel patterns from massive mobile phone network data on basis of location activity motifs (LAMs), which are beneficial to combining the locations, activities, and trips in daily activity-travel behaviors. We then discover that the modified number of LAMs over time conforms well to the classic exploration and preferential return (EPR) model after excluding the influence of the number of trips, which reproduces the human activity-travel behavior characteristics on daily scale and indicates that the complex relationship of LAMs exists between workdays and day-offs. Furthermore, we emphasize three categories of correlation patterns while the relationship of LAMs between workdays and day-offs is instantiated using association rules mining algorithm. Ultimately, the regular individual differences and obvious spatial heterogeneity reveal the formation mechanism of correlation patterns. These empirical results contribute to develop different but related transportation strategies between workdays and day-offs by understanding individual daily activity-travel behaviors.

最近，人们越来越有兴趣了解更广泛和更长期的个人活动-旅行行为，跨越数天甚至数周。然而，个人日常活动-旅行行为在多日内的关系仅部分揭示，尤其是在工作日和休息日之间。因此，我们开发了一个有效的框架，以基于位置活动模体（LAM）从海量移动电话网络数据中提取个人日常活动-旅行模式，这有利于将日常活动-旅行行为中的位置、活动和行程结合起来。然后我们发现在排除旅行次数的影响后，随时间修改的 LAM 数量与经典的探索和优先回报（EPR）模型非常吻合，再现了人类活动-出行行为特征的日尺度，表明工作日和休息日之间存在 LAM 的复杂关系。此外，我们强调了三类相关模式，同时使用关联规则挖掘算法实例化了工作日和休息日之间的 LAM 关系。最终，规则的个体差异和明显的空间异质性揭示了相关模式的形成机制。这些实证结果有助于通过了解个人日常活动 - 旅行行为，在工作日和休息日之间制定不同但相关的交通策略。我们强调了三类关联模式，同时使用关联规则挖掘算法实例化了工作日和休息日之间的 LAM 关系。最终，规则的个体差异和明显的空间异质性揭示了相关模式的形成机制。这些实证结果有助于通过了解个人日常活动 - 旅行行为，在工作日和休息日之间制定不同但相关的交通策略。我们强调了三类关联模式，同时使用关联规则挖掘算法实例化了工作日和休息日之间的 LAM 关系。最终，规则的个体差异和明显的空间异质性揭示了相关模式的形成机制。这些实证结果有助于通过了解个人日常活动 - 旅行行为，在工作日和休息日之间制定不同但相关的交通策略。