当前位置: X-MOL 学术GeoInformatica › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A Framework for Group Converging Pattern Mining using Spatiotemporal Trajectories
GeoInformatica ( IF 2 ) Pub Date : 2020-04-25 , DOI: 10.1007/s10707-020-00404-z
Bin Zhao , Xintao Liu , Jinping Jia , Genlin Ji , Shengxi Tan , Zhaoyuan Yu

A group event such as human and traffic congestion can be very roughly divided into three stages: converging stage before congestion, gathered stage when congestion happens, and dispersing stage that congestion disappears. It is of great interest in modeling and identifying converging behaviors before gathered events actually happen, which helps to proactively predict and handle potential public incidents such as serious stampedes. However, most of existing literature put too much emphasis on the second stage, only a few of them is dedicated to the first stage. In this paper, we propose a novel group pattern, namely converging, which refers to a group of moving objects converging from different directions during a certain period before gathered. To discover efficiently such converging patterns, we develop a framework for converging pattern mining (CPM) by examining how moving objects form clusters and the process of the “cluster containment”. The framework consists of three phases: snapshot cluster discovery phase, cluster containment join phase, and converging detection phase. As cluster containment mining is the key step, we develop three algorithms to discover cluster containment matches: a containment-join-algorithm, called SSCCJ, by using spatial proximity; a signature tree-based cluster-containment-join-algorithm, called STCCJ, which takes advantage of the cluster containment relations and signature techniques to filter enormous unqualified candidates in an efficient and effective way; and third, to keep the advantages of the above algorithms while avoiding their flaws, we further propose a signature quad-tree based cluster-containment-join algorithm, called SQTCCJ, which can identify efficiently matches by considering cluster spatial proximity as well as containment relations simultaneously. To assess the proposed methods, we redefine two evaluation metrics based on the concept of “Precision and Recall” in the field of information retrieval and the characteristics of converging patterns. We also propose a new indicator for measuring the duration of the converging stage in a group event. Finally, the effectiveness of the CPM and the efficiency of the mining algorithms are evaluated using three types of trajectory datasets, and the results show that the SQTCCJ algorithm demonstrates a superior performance.



中文翻译:

基于时空轨迹的群体融合模式挖掘框架

诸如人和交通拥堵之类的团体事件可以大致分为三个阶段:拥塞前的会聚阶段,拥塞发生时的聚集阶段以及拥塞消失的分散阶段。在收集的事件实际发生之前对建模和识别聚合行为非常感兴趣,这有助于主动预测和处理潜在的公共事件,例如严重的踩踏事件。但是,大多数现有文献过多地强调了第二阶段,只有少数文献专门针对第一阶段。在本文中,我们提出了一种新颖的分组模式,即会聚,它是指一组运动物体在聚集之前的一定时期内从不同方向会聚。为了有效地发现这种融合模式,我们通过研究移动对象如何形成集群以及“集群遏制”的过程,开发了一种用于融合模式挖掘(CPM)的框架。该框架包括三个阶段:快照集群发现阶段,集群包含联接阶段和聚合检测阶段。由于集群包含挖掘是关键步骤,因此,我们开发了三种算法来发现集群包含匹配项:通过使用空间邻近度,一个称为SSCCJ的包含-连接算法;以及使用“空间接近度”创建一个名为SSCCJ的包含-连接算法。一种基于签名树的基于聚类的群集包含连接算法,称为STCCJ,它利用聚类包含关系和签名技术来高效,有效地过滤大量不合格的候选对象;第三,在保持上述算法优势的同时避免缺陷,我们还提出了一种基于签名四叉树的基于签名的群集包含-连接算法,称为SQTCCJ,该算法可以通过同时考虑群集空间接近度和包含关系来有效地识别匹配项。为了评估所提出的方法,我们基于信息检索领域的“精确和召回”的概念以及融合模式的特征重新定义了两个评估指标。我们还提出了一种新的指标,用于衡量小组活动中会聚阶段的持续时间。最后,使用三种类型的轨迹数据集评估了CPM的有效性和挖掘算法的效率,结果表明SQTCCJ算法具有优越的性能。通过同时考虑群集空间接近度和包含关系,可以有效地识别匹配项。为了评估所提出的方法,我们基于信息检索领域的“精确和召回”的概念以及融合模式的特征重新定义了两个评估指标。我们还提出了一种新的指标,用于衡量小组活动中会聚阶段的持续时间。最后,使用三种类型的轨迹数据集评估了CPM的有效性和挖掘算法的效率,结果表明SQTCCJ算法具有优越的性能。通过同时考虑群集空间接近度和包含关系,可以有效地识别匹配项。为了评估所提出的方法,我们基于信息检索领域的“精确和召回”的概念和融合模式的特征重新定义了两个评估指标。我们还提出了一种新的指标,用于衡量小组活动中会聚阶段的持续时间。最后,使用三种类型的轨迹数据集评估了CPM的有效性和挖掘算法的效率,结果表明SQTCCJ算法具有优越的性能。我们在信息检索领域基于“精确和召回”的概念和融合模式的特征重新定义了两个评估指标。我们还提出了一种新的指标,用于衡量小组活动中会聚阶段的持续时间。最后,使用三种类型的轨迹数据集评估了CPM的有效性和挖掘算法的效率,结果表明SQTCCJ算法具有优越的性能。我们在信息检索领域基于“精确和召回”的概念和融合模式的特征重新定义了两个评估指标。我们还提出了一种新的指标,用于衡量小组活动中会聚阶段的持续时间。最后,使用三种类型的轨迹数据集评估了CPM的有效性和挖掘算法的效率,结果表明SQTCCJ算法具有优越的性能。

更新日期:2020-04-25
down
wechat
bug