Institute of Computing Technology, Chinese Academy IR
Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems | |
Gu, Jingjing1; Zhou, Qiang1; Yang, Jingyuan2; Liu, Yanchi3; Zhuang, Fuzhen4,5,6; Zhao, Yanchao1; Xiong, Hui3 | |
2022-02-01 | |
发表期刊 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING |
ISSN | 1041-4347 |
卷号 | 34期号:2页码:640-652 |
摘要 | Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility. However, the flexibility of these dockless systems comes at the cost of management and operation complexity. Indeed, the imbalanced and dynamic use of bikes leads to mandatory rebalancing operations, which impose a critical need for effective bike traffic flow prediction. While efforts have been made in developing traffic flow prediction models, existing approaches lack interpretability, and thus have limited value in practical deployment. To this end, we propose an Interpretable Bike Flow Prediction (IBFP) framework, which can provide effective bike flow prediction with interpretable traffic patterns. Specifically, by dividing the urban area into regions according to flow density, we first model the spatio-temporal bike flows between regions with graph regularized sparse representation, where graph Laplacian is used as a smooth operator to preserve the commonalities of the periodic data structure. Then, we extract traffic patterns from bike flows using subspace clustering with sparse representation to construct interpretable base matrices. Moreover, the bike flows can be predicted with the interpretable base matrices and learned parameters. Finally, experimental results on real-world data show the advantages of the IBFP method for flow prediction in dockless bike sharing systems. In addition, the interpretability of our flow pattern exploitation is further illustrated through a case study where IBFP provides valuable insights into bike flow analysis. |
关键词 | Sparse matrices Urban areas Satellite broadcasting Redundancy Predictive models Data models Matrix converters Dockless bike sharing system pattern exploitation interpretable base matrices flow prediction |
DOI | 10.1109/TKDE.2020.2988008 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61572253] ; National Natural Science Foundation of China[61602238] ; National Natural Science Foundation of China[U1836206] ; National Natural Science Foundation of China[61773361] |
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:000742180100011 |
出版者 | IEEE COMPUTER SOC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.204/handle/2XEOYT63/18272 |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Gu, Jingjing |
作者单位 | 1.Nanjing Univ Aeronaut & Astronaut, Dept Comp Sci & Technol, Nanjing 210016, Peoples R China 2.George Mason Univ, Fairfax, VA 22030 USA 3.Rutgers State Univ, New Brunswick, NJ 08901 USA 4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100864, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100195, Peoples R China 6.Zhengzhou Univ, Henan Inst Adv Technol, Zhengzhou 450001, Henan, Peoples R China |
推荐引用方式 GB/T 7714 | Gu, Jingjing,Zhou, Qiang,Yang, Jingyuan,et al. Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2022,34(2):640-652. |
APA | Gu, Jingjing.,Zhou, Qiang.,Yang, Jingyuan.,Liu, Yanchi.,Zhuang, Fuzhen.,...&Xiong, Hui.(2022).Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,34(2),640-652. |
MLA | Gu, Jingjing,et al."Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 34.2(2022):640-652. |
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