Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems

doi:10.1109/TKDE.2020.2988008

	Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems
	Gu, Jingjing 1; Zhou, Qiang 1; Yang, Jingyuan 2; Liu, Yanchi 3; Zhuang, Fuzhen 4,5,6; Zhao, Yanchao 1; Xiong, Hui 3
	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	被引频次：8[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/2XEOYT63/18272
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.