Data-Driven Geofencing Design for Point-of-Interest Notifiers Utilizing Genetic Algorithm

• Published in: ISPRS international journal of geo-information Vol. 13; no. 6; p. 174
• Main Authors: Sasaki, Iori, Arikawa, Masatoshi, Lu, Min, Utsumi, Tomihiro, Sato, Ryo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.05.2024
• Subjects: Algorithms; Case studies; cost effectiveness; Design; Designers; flexible circular geofence; Flow distribution; Flow pattern; genetic algorithm; Genetic algorithms; Genetic research; geo-notification; Geofences; geofencing; Global positioning systems; GPS; GPS trajectory; Quality of service; Reliability; Subject specialists; surveys; Tourism; Tourists; Trajectories; trigger event separation; Usability; User experience; Japan; United States > US
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi13060174

This study proposes a method for generating geofences driven by GPS trajectory data to realize scalable point-of-interest (POI) notifiers, encouraging walking tourists to discover new local spots. The case study revealed that manual geofence settings degrade the location relevance and user coverage—key objectives of POI notifiers—and hinder the scalability and reliability of services. The formalization presented computationally equips geofence designers with practical solutions through two implementations based on prior GPS trajectory logs: (1) a multiobjective genetic algorithm that suggests cost-effective geofences by providing trade-off visualizations and (2) a user coverage-penalized genetic algorithm that determines an optimal geofence based on the designers’ expectations. The feasibility and stability of the proposed implementations were tested in areas with varying tourist flow patterns. A comparative survey among manual settings, settings incorporating a reliability simulation, and data-driven settings demonstrates significant performance improvements for geofence services.