부산대학교 도서관

How to effectively extract identifiable information from human mobility data and distinguish different agents is a significant topic for location-based services and intelligent transportation systems, which is described as the Human Mobility Signature Identification problem. A deeper understanding of the identifiable information underlain in human mobility can help us lay the foundation for applications such as irregular user behavior detection and privacy protection. However, human mobility comprises a mixture of different mobility patterns, traditional methods usually pay more attention to spatial-temporal features, while pattern dimension feature is usually ignored, which makes the result very dependent on the population agglomeration degree. To bridge the research gap, in this paper, we propose a novel Life-Travel pattern-based learning module (LTP-Net), in which spatial-temporal-pattern dimension features are embedded together to provide more comprehensive information for individual identification. A real-world mobile phone location dataset is utilized to evaluate the performance of the proposed LTP-Net and traditional methods. Several case studies are also conducted to analyze the model performance, including the abnormal behavior detection for the east Japan earthquake.