? 用户异源移动轨迹链接的研究
Journal of Computer Science and Technology
Quick Search in JCST
 Advanced Search 
      Home | PrePrint | SiteMap | Contact Us | Help
 
Indexed by   SCIE, EI ...
Bimonthly    Since 1986
Journal of Computer Science and Technology 2018, Vol. 33 Issue (4) :792-806    DOI: 10.1007/s11390-018-1856-x
Special Issue on Software Engineering for High-Confidence Systems << Previous Articles | Next Articles >>
用户异源移动轨迹链接的研究
Guo-Wei Wang, Jin-Dou Zhang, Jing Li, Member, CCF, ACM
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
Complete Your Mobility: Linking Trajectories Across Heterogeneous Mobility Data Sources
Guo-Wei Wang, Jin-Dou Zhang, Jing Li, Member, CCF, ACM
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China

摘要
参考文献
相关文章
Download: [PDF 415KB]  
摘要 现今社会中,人类的活动和移动被各种方式记录,形成了彼此隔离的不同轨迹集。因此,将一个人在不同轨迹集中地不同轨迹链接起来是非常重要的,这样可以为轨迹挖掘任务提供更丰富的用户信息。轨迹链接的大多数现有的工作仅利用一维信息来链接轨迹,并且以一对多的方式链接轨迹(为一个特定轨迹提供若干候选可链接轨迹)。而在本文中,我们提出了一种称为使用多维信息的一对一轨迹链接(OCTL)的新方法,该方法将不同集合中的一个人的相应轨迹一对一的进行链接。我们首先提取异源轨迹间的多维相关性特征,包括空间、时间和时空关系,以综合描述轨迹之间的关系。使用这些特征,我们计算每对异源轨迹属于同一用户的概率。然后,我们将根据概率推理可链接轨迹的问题抽象为二分图匹配问题,并采用有效的方法将一个轨迹链接到另一个轨迹。最终,我们在两个现实世界的轨迹集合上对比了我们的方法和已有的方法,实验结果显示我们的方法具有明显的优势。
关键词轨迹链接   轨迹数据挖掘   轨迹相似度   移动模式挖掘     
Abstract: Nowadays, human activities and movements are recorded by a variety of tools, forming different trajectory sets which are usually isolated from one another. Thus, it is very important to link different trajectories of one person in different sets to provide massive information for facilitating trajectory mining tasks. Most prior work took advantages of only one dimensional information to link trajectories and can link trajectories in a one-to-many manner (providing several candidate trajectories to link to one specific trajectory). In this paper, we propose a novel approach called one-to-one constraint trajectory linking with multi-dimensional information (OCTL) that links the corresponding trajectories of one person in different sets in a one-to-one manner. We extract multidimensional features from different trajectory datasets for corresponding relationships prediction, including spatial, temporal and spatio-temporal information, which jointly describe the relationships between trajectories. Using these features, we calculate the corresponding probabilities between trajectories in different datasets. Then, we formulate the link inference problem as a bipartite graph matching problem and employ effective methods to link one trajectory to another. Moreover, the advantages of our approach are empirically verified on two real-world trajectory sets with convincing results.
Keywordstrajectory linking   trajectory data mining   trajectory similarity   mobility pattern mining     
Received 2017-05-09;
本文基金:

The work was supported by the National Key Research and Development Program of China under Grant No. 2016YFB0201402.

About author: Guo-Wei Wang is a Ph.D. student in School of Computer Science and Technology at University of Science and Technology of China (USTC), Hefei. He received his Bachelor's degree at USTC, Hefei, in 2012. His current research interests include trajectory data mining and cloud computing.
引用本文:   
Guo-Wei Wang, Jin-Dou Zhang, Jing Li.用户异源移动轨迹链接的研究[J]  Journal of Computer Science and Technology , 2018,V33(4): 792-806
Guo-Wei Wang, Jin-Dou Zhang, Jing Li.Complete Your Mobility: Linking Trajectories Across Heterogeneous Mobility Data Sources[J]  Journal of Computer Science and Technology, 2018,V33(4): 792-806
链接本文:  
http://jcst.ict.ac.cn:8080/jcst/CN/10.1007/s11390-018-1856-x
Copyright 2010 by Journal of Computer Science and Technology