한국융합학회논문지 (Journal of the Korea Convergence Society)

  • 제10권4호
  • /
  • Pages.33-38
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  • 2019
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  • 2233-4890(pISSN)
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  • 2713-6353(eISSN)
한국융합학회 (Korea Convergence Society)
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이동 객체의 궤적 처리를 위한 색인 구조 및 궤적 데이터 생성 알고리즘

Index Structure and Trajectory Data Generation Algorithm to Process the Trajectory of Moving Object

  • 채철주 (한국농수산대학 교양공통과) ;
  • 김용기 (전주비전대학교 IT융합시스템과)
  • Chae, Cheol-Joo (Dept. of General Education, Korea National College of Agriculture and Fisheries) ;
  • Kim, Yong-Ki (Dept. of IT Convergence System Engineering, VISION College of JeonJu)
  • 투고 : 2019.01.16
  • 심사 : 2019.04.20
  • 발행 : 2019.04.28
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⟨ 이전 논문 다음 논문 ⟩

초록

최근 다양한 LBS(location-based service) 서비스를 지원하기 위해 실제 공간 네트워크를 고려한 연구가 활발하게 진행 중이다. 이를 위해, 도로 네트워크에서 데이터 처리를 위한 실험 데이터가 다수 존재한다. 그러나 이러한 이동 객체의 궤적을 처리하기 위한 데이터는 이용하기에 적합하지 않다. 따라서 본 논문에서는 도로 네트워크 환경에서 궤적 데이터를 처리할 수 있는 색인 구조와 궤적 데이터 생성 알고리즘을 제안한다. 또한, 제안하는 구조와 알고리즘의 우수성을 입증하기 위해, 샌프란시스코 맵으로부터 만들어진 데이터를 이용하여 제안하는 알고리즘을 통해 에지 기반의 궤적 데이터를 생성됨을 보인다.

Recently, to support location-based services, there have been many researches which consider the spatial network. For this, there are many experimental data for data processing on the road network. However, the data to process the trajectory of moving objects are not suitable. Therefore, we propose index structure to process the trajectory data on the road network and the trajectory data generation algorithm. In addition, to prove efficiency of our index structure and algorithm, we show that edge-based trajectory data are generated through the proposed algorithm using the map data of San Francisco Bay.

키워드

OHHGBW_2019_v10n4_33_f0001.png 이미지

Fig. 1. Example of location based service on road network

OHHGBW_2019_v10n4_33_f0002.png 이미지

Fig. 2. Classification of research dealing with moving objects

OHHGBW_2019_v10n4_33_f0003.png 이미지

Fig. 3. Road network and moving objects (or Points of Interest)

OHHGBW_2019_v10n4_33_f0004.png 이미지

Fig. 4. Network Index Structure and Relationship between Edge and Trajectory

OHHGBW_2019_v10n4_33_f0005.png 이미지

Fig. 7. Data generation by moving object generator

OHHGBW_2019_v10n4_33_f0006.png 이미지

Fig. 5. Insertion algorithm of moving objects

OHHGBW_2019_v10n4_33_f0007.png 이미지

Fig. 6. Edge based trajectory generation algorithm

Table 1. Mobile phone ownership over time

OHHGBW_2019_v10n4_33_t0001.png 이미지

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