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

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—...

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Vydáno v:ISPRS international journal of geo-information Ročník 13; číslo 6; s. 174
Hlavní autoři: Sasaki, Iori, Arikawa, Masatoshi, Lu, Min, Utsumi, Tomihiro, Sato, Ryo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 25.05.2024
Témata:
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
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Abstract 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.
AbstractList 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.
Audience Academic
Author Sasaki, Iori
Sato, Ryo
Lu, Min
Arikawa, Masatoshi
Utsumi, Tomihiro
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  surname: Sasaki
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  givenname: Tomihiro
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  surname: Sato
  fullname: Sato, Ryo
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SubjectTerms 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
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