In the past decade or so, advances in positioning technologies for capturing individual movement have given rise to a wide range of studies, including transportation, tourism, and public health. In particular, considerable effort has been made to characterize human activity–travel patterns from space–time trajectories. In contrast to visualization, geometric, or statistical methods, we propose an approach based on sequential pattern mining for analyzing human activity–travel patterns. To quantify the differences between individuals and population subgroups, we first develop a single sequential similarity measure for assessing the differences between two activity–travel patterns, then extend it to the group level with the capability to compute on two pattern sets. We also develop and implement three Bayesian network models with specially designed topology to predict the forthcoming activity at the individual level. The proposed method achieves similar or better prediction accuracy and is more robust for exploring imbalanced or sparse datasets when compared to other machine learning algorithms.

中文翻译：

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使用贝叶斯网络挖掘顺序活动-旅行模式以进行个人级别的人类活动预测

在过去的十年左右的时间里，用于捕获个人运动的定位技术的发展引起了广泛的研究，包括交通，旅游和公共卫生。特别是，人们已经做出了巨大的努力来表征人类活动-时空轨迹的旅行模式。与可视化，几何或统计方法相比，我们提出了一种基于顺序模式挖掘的方法来分析人类活动-旅行模式。为了量化个体和人群亚组之间的差异，我们首先开发了一个连续的相似性度量，以评估两个活动-旅行模式之间的差异，然后将其扩展到组级别，并具有在两个模式集上进行计算的能力。我们还开发并实现了三个贝叶斯网络模型，这些模型具有经过特殊设计的拓扑结构，可以预测各个级别的活动。与其他机器学习算法相比，该方法可实现相似或更好的预测精度，并且对于探索不平衡或稀疏的数据集更健壮。