Predicting future locations of moving objects with deep fuzzy-LSTM networks
文献类型：期刊论文
| 作者 | Li, Mingxiao1,2; Lu, Feng1,2,3,4 ; Zhang, Hengcai1,2,3; Chen, Jie2
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| 刊名 | TRANSPORTMETRICA A-TRANSPORT SCIENCE
 |
| 出版日期 | 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. |
| WOS关键词 | TRAJECTORY PREDICTION ; MOBILITY |
| 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研究方向 | Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000508897200008 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 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 |
| 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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