한국ITS학회 논문지 (The Journal of The Korea Institute of Intelligent Transport Systems)

  • 제18권1호
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  • Pages.56-73
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  • 2019
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  • 1738-0774(pISSN)
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  • 2384-1729(eISSN)
한국ITS학회 (The Korea Institute of Intelligent Transport Systems)
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개별차량 및 5분 집계 프로브 자료를 이용한 실시간 교통정보 제공

Real-Time Traffic Information Provision Using Individual Probe and Five-Minute Aggregated Data

  • 장진환 (한국건설기술연구원 도로연구소)
  • Jang, Jinhwan (Dept. of Highway Res., Korea Inst. of Civil Eng. and Building Tech.)
  • 투고 : 2018.12.01
  • 심사 : 2019.02.22
  • 발행 : 2019.02.28
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초록

무선통신기기 보급 확대로 인해 프로브 기반 교통정보시스템이 확대 구축되고 있다. 프로브 기반 통행시간 정보의 시간 처짐 현상 극복을 위해 다수의 예측 기법들이 적용되고 있지만, 일별 및 요일별 교통패턴이 불규칙한 구간에서는 예측 기법의 효용성이 저하되는 것으로 알려져 있다. 이로 인해 불규칙한 교통패턴을 나타내는 구간에서는 일반적으로 5분 집계단위의 프로브 정보를 사용하는데, 이는 집계 시간간격만큼 시간 처짐 현상을 증대시킨다. 이에 본 연구에서는 통행시간 패턴이 불규칙한 구간에 적용 가능한 교통정보 제공 방법론을 제안하였다. 제안된 방법은 개별차량 단위 프로브 정보와 5분 집계 프로브 정보를 융합 적용하는 것으로써, 제안된 방법론 적용 시 통행시간 정보 오차를 최대 18%까지 감소시킬 수 있는 것으로 분석되었다.

Probe-based systems have been gaining popularity in advanced traveler information systems. However, the high possibility of providing inaccurate travel-time information due to the inherent time-lag phenomenon is still an important issue to be resolved. To mitigate the time-lag problem, different prediction techniques have been applied, but the techniques are generally regarded as less effective for travel times with high variability. For this reason, current 5-min aggregated data have been commonly used for real-time travel-time provision on highways with high travel-time fluctuation. However, the 5-min aggregation interval itself can further increase the time-lags in the real-time travel-time information equivalent to 5 minutes. In this study, a new scheme that uses both individual probe and 5-min aggregated travel times is suggested to provide reliable real-time travel-time information. The scheme utilizes individual probe data under congested conditions and 5-min aggregated data under uncongested conditions, respectively. As a result of an evaluation with field data, the proposed scheme showed the best performance, with a maximum reduction in travel-time error of 18%.

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