Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling

Nurretin Dorukhan Sergin (), Jiuyun Hu (), Ziyue Li (), Chen Zhang (), Fugee Tsung () and Hao Yan ()
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Nurretin Dorukhan Sergin: School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona 85281
Jiuyun Hu: School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona 85281
Ziyue Li: Cologne Institute for Information Systems, University of Cologne, 50923 Cologne, Germany; and EWI gGmbH, University of Cologne, 50923 Cologne, Germany
Chen Zhang: Department of Industrial Engineering, Tsinghua University, Beijing 100190, China
Fugee Tsung: Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Hong Kong; and Information Hub, Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511457, China
Hao Yan: School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona 85281

INFORMS Joural on Data Science, 2025, vol. 4, issue 1, 33-50

Abstract: Tensor clustering has become an important topic, specifically in spatiotemporal modeling, because of its ability to cluster spatial modes (e.g., stations or road segments) and temporal modes (e.g., time of day or day of the week). Our motivating example is from subway passenger flow modeling, where similarities between stations are commonly found. However, the challenges lie in the innate high-dimensionality of tensors and also the potential existence of anomalies. This is because the three tasks, that is, dimension reduction, clustering, and anomaly decomposition, are intercorrelated with each other, and treating them in a separate manner will render a suboptimal performance. Thus, in this work, we design a tensor-based subspace clustering and anomaly decomposition technique for simultaneous outlier-robust dimension reduction and clustering for high-dimensional tensors. To achieve this, a novel low-rank robust subspace clustering decomposition model is proposed by combining Tucker decomposition, sparse anomaly decomposition, and subspace clustering. An effective algorithm based on Block Coordinate Descent is proposed to update the parameters. Prudent experiments prove the effectiveness of the proposed framework via the simulation study, with a gain of +25% clustering accuracy over benchmark methods in a hard case. The interrelations of the three tasks are also analyzed via ablation studies, validating the interrelation assumption. Moreover, a case study in station clustering based on real passenger flow data is conducted, with quite valuable insights discovered.

Keywords: subspace clustering; tensor decomposition; anomaly detection; spatiotemporal analysis (search for similar items in EconPapers)
Date: 2025
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