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A Stay Detection Algorithm Using GPS Trajectory and Points of Interest Data

첫 페이지 보기
  • 발행기관
    국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
  • 간행물
    International Journal of Internet, Broadcasting and Communication 바로가기
  • 통권
    Vol.15 No.3 (2023.08)바로가기
  • 페이지
    pp.176-184
  • 저자
    Eunchong Koh, Changhoon Lyu, Goya Choi, Kye-Dong Jung, Soonchul Kwon, Chigon Hwang
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A435280

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원문정보

초록

영어
Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.

목차

Abstract
1. Introduction
2. Related research
2-1. Location techniques
2-2. Nonlinear SVM and K-means clustering
3. System for applying the proposed algorithm
3-1. Pre-Processing
3-2. Machine Learning
4. Implementation and evaluation
5. Conclusion
Acknowledgement
References

키워드

POI k-means clustering SVM location estimation GPS

저자

  • Eunchong Koh [ Graduate School of Smart Convergence Kwangwoon University, Korea ]
  • Changhoon Lyu [ LOYQU Inc., 14, Magokjungang 8-ro, Gangseo-gu, Seoul, Republic of Korea ]
  • Goya Choi [ LOYQU Inc., 14, Magokjungang 8-ro, Gangseo-gu, Seoul, Republic of Korea ]
  • Kye-Dong Jung [ Professor, Ingenium College of liberal arts, Kwangwoon University, Republic of Korea ]
  • Soonchul Kwon [ Associate professor, Graduate School of Smart Convergence, Kwangwoon University, Republic of Korea ]
  • Chigon Hwang [ Visiting Professor, Department of Computer Engineering, Institute of Information Technology, Kwangwoon University, Seoul, 01897, Republic of Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
  • 설립연도
    2000
  • 분야
    공학>전자/정보통신공학
  • 소개
    인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.

간행물

  • 간행물명
    International Journal of Internet, Broadcasting and Communication
  • 간기
    계간
  • pISSN
    2288-4920
  • eISSN
    2288-4939
  • 수록기간
    2009~2025
  • 십진분류
    KDC 326 DDC 380

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