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Mining User Mobility Insights from Public Wi-Fi Data Using Association Rules in Urban Riverfront Areas
Author(s)
Matarat, Korakot
Surawanitkun, Chayada
Wongsinlatam, Wullapa
Remsungnen, Tawun
Nokkaew, Manussawee
Michel, Jakub
Wiangwiset, Thalerngsak
Boonkirdram, Sarawoot
Namvong, Ariya
Date Issued
July 1, 2026
Type
Article
Abstract
Understanding user mobility patterns is essential for effective urban planning and resource management. This study employs Association Rule Mining to analyze public Wi-Fi data collected from 11 access points along the Mekong River in Sri Chiang Mai, Thailand, from May 2022 to December 2023. By examining co-occurring movement patterns in over 73.7 million connection records, the research uncovers key insights into human behavior in the area. The findings highlight the area in front of the Fresh Market as a central destination, with confidence values exceeding 0.99 for related movement rules. The results reveal pronounced temporal variations in movement patterns, with transitions from commercial areas in the mornings to leisure spaces in the afternoons and evenings. Weekday patterns differ notably from weekend behaviors, reflecting how time influences urban space utilization. These insights provide urban planners and policymakers with data-driven evidence to optimize infrastructure development, enhance public spaces, and improve resource allocation. Although this study is limited to Wi-Fi data, it provides significant contributions to the development of smart cities that are more responsive and sustainable, paving the way for improved urban living experiences and more efficient resource management. Future work could integrate multiple data sources to enable more comprehensive mobility analysis and advance sustainable urban development goals.
Citation
Ecti Transactions on Computer and Information Technology, 20(3), 486-499, 2026