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Understanding Park Visitors' Mobility via Passive Wi-Fi Sensing and Data Mining
Conference proceeding

Understanding Park Visitors' Mobility via Passive Wi-Fi Sensing and Data Mining

Zann Koh, Benny Kai Kiat Ng, Sam Conrad Joyce, Belinda Yuen, Chau Yuen and ASSOC COMPUTING MACHINERY
Proceedings of the 19th Workshop on Mobility in the Evolving Internet Architecture, pp.37-42
ACM Conferences
ACM MobiCom '24: The 30th Annual International Conference on Mobile Computing and Networking
18/11/2024
DOI: https://doi.org/10.1145/3691555.3696831

Abstract

Human-centered computing Human-centered computing -- Ubiquitous and mobile computing Information systems Information systems -- Information systems applications Information systems -- Information systems applications -- Data mining Information systems -- Information systems applications -- Spatial-temporal systems Information systems -- Information systems applications -- Spatial-temporal systems -- Location based services Networks Networks -- Network types Networks -- Network types -- Cyber-physical networks Networks -- Network types -- Cyber-physical networks -- Sensor networks
Understanding the mobility patterns of park users is crucial for optimizing urban planning and park management. However, existing methods often rely on intrusive or labor-intensive data collection techniques, which may not be scalable for large public spaces. Our proposed framework includes a temporal analysis of the daily device count patterns across different day types, as well as investigating popular trajectories in a large park. We identified distinct usage patterns across different areas of the parks, which varied according to the type and time of day. Our findings demonstrate the potential of using WiFi signal data as a scalable and non-intrusive method for mobility data collection.
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https://doi.org/10.1145/3691555.3696831View
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