Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems

• URL: http://arxiv.org/abs/2004.05774v1
• Date: Mon, 13 Apr 2020 05:31:50 GMT
• Title: Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems
• Authors: Jingjing Gu, Qiang Zhou, Jingyuan Yang, Yanchi Liu, Fuzhen Zhuang, Yanchao Zhao, and Hui Xiong
• Abstract summary: We propose an Interpretable Bike Flow Prediction (IBFP) framework, which can provide effective bike flow prediction with interpretable traffic patterns. By dividing the urban area into regions according to flow density, we first model the bike flows between regions with graph regularized sparse representation. Then, we extract traffic patterns from bike flows using subspace clustering with sparse representation to construct interpretable base matrices. Finally, experimental results on real-world data show the advantages of the IBFP method for flow prediction in dockless bike sharing systems.
• Score: 45.45179250456602
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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