Transfer Learning for Region-Wide Trajectory Outlier Detection

doi:10.1109/ACCESS.2023.3294689

	Transfer Learning for Region-Wide Trajectory Outlier Detection
	Su, Yueyang 1,2; Yao, Di 1,2; Tian, Tian 3; Bi, Jingping 1,2
	2023
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	11 页码:97001-97013
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.
关键词	Trajectory outlier detection transfer learning VAE spatial-temporal data trajectory data mining
DOI	10.1109/ACCESS.2023.3294689
收录类别	SCI
语种	英语
资助项目	NSFC[62002343] ; NSFC[6207704]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001067561900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21170
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.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, 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.