Predicting future locations of moving objects with deep fuzzy-LSTM networks

doi:10.1080/23249935.2018.1552334

	Predicting future locations of moving objects with deep fuzzy-LSTM networks
	Li, Mingxiao 1,2; Lu, Feng 1,2,3,4; Zhang, Hengcai 1,2,3; Chen, Jie 2
刊名	TRANSPORTMETRICA A-TRANSPORT SCIENCE
	2020-12-20
卷号	16 期号:1 页码:119-136
关键词	Location prediction fuzzy space partition mobile phone data trajectory data mining deep learning
ISSN号	2324-9935
DOI	10.1080/23249935.2018.1552334
通讯作者	Zhang, Hengcai(zhanghc@lreis.ac.cn)
英文摘要	Trajectory prediction plays an important role in supporting many advanced applications such as location-based services and advanced intelligent traffic managements. Most existing trajectory prediction methods employed fixed spatial division and focused on human closeness movement patterns. However, these methods could lead to a sharp boundary limitation and ignore the periodic characteristics of human mobility. This paper proposes a novel trajectory prediction method based on long short-term memory network (LSTM) called the trajectory predictor with fuzzy-long short-term memory network (TrjPre-FLSTM). First, we introduce a new fuzzy trajectory concept and extend the LSTM to a fuzzy-LSTM to overcome the sharp boundary limitation. Second, we explicitly incorporate the periodic movement patterns of moving objects in the trajectory prediction. Using a real-world mobile phone dataset, we evaluate the performance of TrjPre-FLSTM with two latest competitors. The case study results indicate that the proposed method outperforms the comparative methods in terms of the prediction accuracy.
资助项目	National Natural Science Foundation of China[41771436] ; National Natural Science Foundation of China[41771476] ; Key Research Program of the Chinese Academy of Sciences[ZDRW-ZS-2016-6-3] ; Key Research Program of the Chinese Academy of Sciences[ZDRW-ZS-2017-4] ; National Key Research and Development Program[2016YFB0502104]
WOS关键词	TRAJECTORY PREDICTION ; MOBILITY
WOS研究方向	Transportation
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000508897200008
资助机构	National Natural Science Foundation of China ; Key Research Program of the Chinese Academy of Sciences ; National Key Research and Development Program
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/131633]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhang, Hengcai
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China
推荐引用方式 GB/T 7714	Li, Mingxiao,Lu, Feng,Zhang, Hengcai,et al. Predicting future locations of moving objects with deep fuzzy-LSTM networks[J]. TRANSPORTMETRICA A-TRANSPORT SCIENCE,2020,16(1):119-136.
APA	Li, Mingxiao,Lu, Feng,Zhang, Hengcai,&Chen, Jie.(2020).Predicting future locations of moving objects with deep fuzzy-LSTM networks.TRANSPORTMETRICA A-TRANSPORT SCIENCE,16(1),119-136.
MLA	Li, Mingxiao,et al."Predicting future locations of moving objects with deep fuzzy-LSTM networks".TRANSPORTMETRICA A-TRANSPORT SCIENCE 16.1(2020):119-136.