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Transfer Learning for Region-Wide Trajectory Outlier Detection

文献类型：期刊论文

作者Su, Yueyang1,2; Yao, Di1,2; Tian, Tian3; Bi, Jingping1,2
刊名IEEE ACCESS
出版日期2023
卷号11页码:97001-97013
关键词Trajectory outlier detection transfer learning VAE spatial-temporal data trajectory data mining
ISSN号2169-3536
DOI10.1109/ACCESS.2023.3294689
英文摘要Trajectory outlier detection is a crucial task in trajectory data mining and has received significant attention. However, the distribution of trajectories is tied to social activities, resulting in extreme unevenness among regions. While existing methods have demonstrated excellent performance in regions with sufficient historical trajectories, they frequently struggle to detect outliers in regions with limited trajectories. Unfortunately, this issue has not received much attention, leaving a gap in the current understanding of trajectory mining. To deal with this problem, we in this paper propose a model called TTOD that can effectively detect outliers in regions with sparse data by transferring knowledge among regions. The main idea is to learn a feature mapping function that maps the global feature space of auxiliary regions to the target region's specific feature space. To achieve this, we adopt a VAE-based model called the Global VAE to learn the global feature space in auxiliary regions by modeling the trajectory patterns with Gaussian distributions. Then, we propose a Specific-region VAE that serves as the mapping function to learn the target feature space. Additionally, considering the data drift of feature distributions among regions, we introduced an additional pattern synthesis layer, named the De-drift Layer, to diversify the target feature space, thus addressing the pattern missing issue caused by the gap of feature distributions between the auxiliary regions and the target regions. Then the target feature space can be well studied and applied to detect outliers. Finally, we conduct extensive experiments on two real taxi trajectory datasets and the results show that TTOD achieves state-of-the-art performance compared with the baselines.
资助项目NSFC[62002343] ; NSFC[6207704]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:001067561900001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/21169]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Bi, Jingping
作者单位1.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
3.Nanjing Marine Radar Inst, Nanjing 210000, Peoples R China
推荐引用方式
GB/T 7714		 Su, Yueyang,Yao, Di,Tian, Tian,et al. Transfer Learning for Region-Wide Trajectory Outlier Detection[J]. IEEE ACCESS,2023,11:97001-97013.
APA Su, Yueyang,Yao, Di,Tian, Tian,&Bi, Jingping.(2023).Transfer Learning for Region-Wide Trajectory Outlier Detection.IEEE ACCESS,11,97001-97013.
MLA Su, Yueyang,et al."Transfer Learning for Region-Wide Trajectory Outlier Detection".IEEE ACCESS 11(2023):97001-97013.

入库方式： OAI收割

来源：计算技术研究所

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