Predicting Human Mobility With Semantic Motivation via Multi-Task Attentional Recurrent Networks

doi:10.1109/TKDE.2020.3006048

	Predicting Human Mobility With Semantic Motivation via Multi-Task Attentional Recurrent Networks
	Feng, Jie 1; Li, Yong 1; Yang, Zeyu 1; Qiu, Qiang 2; Jin, Depeng 1
	2022-05-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	34 期号:5 页码:2360-2374
摘要	Human mobility prediction is of great importance for a wide spectrum of location-based applications. However, predicting mobility is not trivial because of four challenges: 1) the complex sequential transition regularities exhibited with time-dependent and high-order nature; 2) the multi-level periodicity of human mobility; 3) the heterogeneity and sparsity of the collected trajectory data; and 4) the complicated semantic motivation behind the mobility. In this paper, we propose DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories. In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern human mobility. Then, we propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way, which effectively utilizes the periodicity nature to augment the recurrent neural network for mobility prediction. Furthermore, we design a context adaptor to capture the semantic effects of Point-Of-Interest (POI)-based activity and temporal factor (e.g., dwell time). Finally, we use the multi-task framework to encourage the model to learn comprehensive motivations with mobility by introducing the task of the next activity type prediction and the next check-in time prediction. We perform experiments on four representative real-life mobility datasets, and extensive evaluation results demonstrate that our model outperforms the state-of-the-art models by more than 10 percent. Moreover, compared with the state-of-the-art neural network models, DeepMove provides intuitive explanations into the prediction and sheds light on interpretable mobility prediction.
关键词	Trajectory Semantics Predictive models Task analysis Recurrent neural networks Adaptation models Context modeling Neural network attention human mobility
DOI	10.1109/TKDE.2020.3006048
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFB1800804] ; National Nature Science Foundation of China[U1936217] ; National Nature Science Foundation of China[61971267] ; National Nature Science Foundation of China[61972223] ; National Nature Science Foundation of China[61941117] ; National Nature Science Foundation of China[61861136003] ; Beijing Natural Science Foundation[L182038] ; Beijing National Research Center for Information Science and Technology[20031887521] ; Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000777332000026
出版者	IEEE COMPUTER SOC
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18913
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Yong
作者单位	1.Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol BNRis, Dept Elect Engn, Beijing 100084, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100084, Peoples R China
推荐引用方式 GB/T 7714	Feng, Jie,Li, Yong,Yang, Zeyu,et al. Predicting Human Mobility With Semantic Motivation via Multi-Task Attentional Recurrent Networks[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2022,34(5):2360-2374.
APA	Feng, Jie,Li, Yong,Yang, Zeyu,Qiu, Qiang,&Jin, Depeng.(2022).Predicting Human Mobility With Semantic Motivation via Multi-Task Attentional Recurrent Networks.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,34(5),2360-2374.
MLA	Feng, Jie,et al."Predicting Human Mobility With Semantic Motivation via Multi-Task Attentional Recurrent Networks".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 34.5(2022):2360-2374.