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

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Estimation of Incident Detection Time on Expressways Based on Market Penetration Rate of Connected Vehicles

커넥티드 차량 보급률 기반 고속도로 돌발상황 검지시간 추정

  • Sanggi Nam (Dept. of Urban Planning and Eng., Univ. of Yeungnam) ;
  • Younshik Chung (Dept. of Urban Planning and Eng., Univ. of Yeungnam) ;
  • Hoekyoung Kim (Dept. of Urban Planning and Eng., Univ. of Dong-A) ;
  • Wonggil Kim (Dept. of Mobility, KAIA)
  • 남상기 (영남대학교 도시공학과 ) ;
  • 정연식 (영남대학교 도시공학과 ) ;
  • 김회경 (동아대학교 도시공학과 ) ;
  • 김원길 (국토교통과학기술진흥원 모빌리티본부)
  • Received : 2023.05.02
  • Accepted : 2023.05.26
  • Published : 2023.06.30
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Abstract

Recent advances in artificial intelligence (AI) technology have enabled the integration of AI technology into image sensors, such as Closed-Circuit Television (CCTV), to detect specific traffic incidents. However, most incident detection methods have been carried out using fixed equipment. Therefore, there have been limitations to incident detection for all roadways. Nevertheless, the development of mobile image collection and analysis technology, such as image sensors and edge-computing, is spreading. The purpose of this study is to estimate the reducing effect of the incident detection time according to the introduction level of mobile image collection and analysis equipment (or connected vehicles). To carry out this purpose, we utilized data on the number of incidents collected by the Suwon branch of the Gyeongbu expressway in 2021. The analysis results showed that if the market penetration rate (MPR) of connected vehicles is 4% or higher for two-lane expressway and 3% or higher for three-lane expressways, the incident detection time was less than one minute. Furthermore, if the MPR is 0.4% or higher for two-lane expressways and 0.2% or higher for three-lane expressways, the incident detection time decreased compared to the average incident detection time announced by the Korea Expressway Corporation for both two-lane and three-lane expressways.

최근 인공지능 (Artificial Intelligence: AI) 기술 발전으로 폐쇄회로 TV(Closed-Circuit television: CCTV)와 같은 영상 센서에 AI 기술을 도입하여 특정 돌발상황을 검지하고 있으나 대부분 고정식 장비 기반으로 돌발상황 검지가 진행되어왔다. 따라서 모든 도로 공간에 대한 돌발상황 검지에는 한계가 존재해왔다. 그러나 영상 센서와 edge-computing 기술 등의 발전으로 이동식 영상정보수집 및 분석 기술이 확산되고 있다. 본 연구는 이러한 이동식 영상 수집 및 분석 장비(커넥티드 차량)의 도입 수준에 따른 돌발상황 검지시간 감소효과를 추정하는 것이 목적이다. 이를 위해 2021년 경부고속도로 수원지사에서 수집된 돌발상황 발생 건수 자료를 활용하였다. 분석 결과 편도 2차로 고속도로는 커넥티드 차량의 보급률(Market Penetration Rate: MPR)이 4% 이상, 편도 3차로 고속도로는 3% 이상이면 돌발상황 검지 시간이 1분 이하로 나타났고, 편도 2차로와 편도 3차로 고속도로에서 MPR이 각각 0.4% 이상, 0.2% 이상이면 한국도로공사에서 발표한 평균 돌발상황 검지시간 보다 감소하는 것으로 나타났다.

Keywords

Acknowledgement

This work was supported by the 2023 Yeungnam University research grant.

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