Understanding Residents’ Behavior for Smart City Management by Sequential and Periodic Pattern Mining

Ma, Cong; Vu, Huy Quan; Wang, Jinlong; Trieu, Van-Hau; Li, Gang

doi:10.1109/tcss.2023.3249740

Cited by 3 publications

(2 citation statements)

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“…To address this issue, researchers have turned their attention to investigating the periodicity of more abstract aspects of human mobility. These include periodicity through location clustering ( Yuan et al, 2017b ; Ma et al, 2023 ) and location semantic periodicity ( Zhang, Lee & Lee, 2019 ). Among these abstract aspects, the concept of mobility intention has been introduced to explain why individuals appear at specific locations at particular times ( Hu, Jamali & Ester, 2013 ; Yuan et al, 2013 ; Liu et al, 2015 ).…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

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Mining human periodic behaviors via tensor factorization and entropy

Yi,

Su,

et al. 2024

PeerJ Computer Science

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Understanding human periodic behaviors is crucial in many applications. Existing research has shown the existence of periodicity in human behaviors, but has achieved limited success in leveraging location periodicity and obtaining satisfactory accuracy for oscillations in human periodic behaviors. In this article, we propose the Mobility Intention and Relative Entropy (MIRE) model to address these challenges. We employ tensor decomposition to extract mobility intentions from spatiotemporal datasets, thereby revealing hidden structures in users’ historical records. Subsequently, we utilize subsequences associated with the same mobility intention to mine human periodic behaviors. Furthermore, we introduce a novel periodicity detection algorithm based on relative entropy. Our experimental results, conducted on real-world datasets, demonstrate the effectiveness of the MIRE model in accurately uncovering human periodic behaviors. Comparative analysis further reveals that the MIRE model significantly outperforms baseline periodicity detection algorithms.

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Section: Preliminaries and Related Workmentioning

confidence: 99%

“…In recent years, researchers have investigated mining human periodic behaviors based on location clusters with similar types ( Yuan et al, 2017b ; Ma et al, 2023 ) or semantics ( Zhang, Lee & Lee, 2019 ). However, two significant barriers currently hinder the accurate mining of human periodic behaviors.…”

Section: Introductionmentioning

confidence: 99%