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

Understanding the residents' routine and repetitive behavior patterns is important for city planners and strategic partners to enact appropriate city management policies. However, the existing approaches reported in smart city management areas often rely on clustering or machine learning, which...

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Bibliographic Details
Published in:IEEE transactions on computational social systems 2024-02, Vol.11 (1), p.1-17
Main Authors: Ma, Cong, Vu, Huy Quan, Wang, Jinlong, Trieu, Van-Hau, Li, Gang
Format: Article
Language:English
Subjects:
Behavioral sciences
Cities
Clustering
Data analysis
Data collection
Data mining
Datasets
Machine learning
Pandemics
Pattern analysis
Periodic pattern mining
sequential pattern mining
Smart cities
smart city
social media analytics
Soft sensors
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Summary:Understanding the residents' routine and repetitive behavior patterns is important for city planners and strategic partners to enact appropriate city management policies. However, the existing approaches reported in smart city management areas often rely on clustering or machine learning, which are ineffective in capturing such behavioral patterns. Aiming to address this research gap, this article proposes an analytical framework, adopting sequential and periodic pattern mining techniques, to effectively discover residents' routine behavior patterns. The effectiveness of the proposed framework is demonstrated in a case study of American public behavior based on a large-scale venue check-in dataset. The dataset was collected in 2020 (during the global pandemic due to COVID-19) and contains 257 561 check-in data of 3995 residents. The findings uncovered interesting behavioral patterns and venue visit information of residents in the United States during the pandemic, which could help the public and crisis management in cities.
ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2023.3249740