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

  • 제22권1호
  • /
  • Pages.16-24
  • /
  • 2023
  • /
  • 1738-0774(pISSN)
  • /
  • 2384-1729(eISSN)
한국ITS학회 (The Korea Institute of Intelligent Transport Systems)
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교통사고 잦은 곳 안전시설 개선 방안 예측 모델 개발

Development of Prediction Model for Improvement of Safety Facilities in Frequent Traffic Accidents

  • 권재경 (아주대학교 일반대학원 교통공학과) ;
  • 김시원 (아주대학교 일반대학원 교통공학과) ;
  • 황재성 (아주대학교 교통연구센터) ;
  • 이재형 (LG유플러스 CITS/자율주행사업팀) ;
  • 이철기 (아주대학교 교통시스템공학과)
  • Jaekyung Kwon (Dept. of Dept. of Transportation Eng., Ajou University) ;
  • Siwon Kim (Dept. of Dept. of Transportation Eng., Ajou University) ;
  • Jae seong Hwang (Dept. of Dept. of Transportation Research Center., Ajou University) ;
  • Jaehyung Lee (Dept. of CITS/Autonomous Driving Business, LG Uplus) ;
  • Choul ki Lee (Dept. of Dept. of Transportation Eng., Ajou University)
  • 투고 : 2022.11.29
  • 심사 : 2022.12.19
  • 발행 : 2023.02.28
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초록

교통사고 잦은 곳 개선사업을 통하여 사고가 크게 감소하고 있다. 이러한 결과는 안전 시설물이 큰 역할을 하고 있다. 교통사고는 여러 가지 원인과 다양한 환경적인 요소로 인하여 발생하게 되는데, 한가지 안전시설물 혹은 기준 없는 시설물 설치로는 개선효과를 얻기 어렵다. 따라서 본 연구는 두 가지 안전시설물의 조합으로 사고유형별 개선효과를 분석하였고, 도로종류, 도로형태, 교통량 등으로 환경적인 요소도 포함하여 특정 지점에 맞는 안전시설물 조합을 예측하는 방법을 제시하였다. 예측은 단순 분류가 가능한 예측 모델들을 결합하여 하나의 강한 예측 모델을 만드는 XGBoost 기법으로 선정하여 진행하였다. 이를 통해 최종적으로 현재까지 교통사고 잦은 곳 개선사업을 통해 긍정적인 효과를 가져다 준 안전시설물과 개선이 필요한 지점에 설치될 안전시설물을 같이 도출하여, 안전시설물 효과분석과 향후 설치지점에 대한 예측방법을 제시하였다.

Accidents are greatly reduced through projects to improve frequent traffic accidents. These results show that safety facilities play a big role. Traffic accidents are caused by various causes and various environmental factors, and it is difficult to achieve improvement effects by installing one safety facility or facilities without standards. Therefore, this study analyzed the improvement effect of each accident type by combining the two safety facilities, and suggested a method of predicting the combination of safety facilities suitable for a specific point, including environmental factors such as road type, road type, and traffic. The prediction was carried out by selecting an XGBoost technique that creates one strong prediction model by combining prediction models that can be simple classification. Through this, safety facilities that have had positive effects through improvement projects and safety facilities to be installed at points in need of improvement were derived, and safety facilities effect analysis and prediction methods for future installation points were presented.

키워드

과제정보

이 논문은 2022년도 정부(경찰청)의 재원으로 과학치안진흥센터의 지원을 받아 수행된 연구임 (No.092021C28S01000, 자율주행 혼재 시 도로교통 통합관제시스템 및 운영기술 개발)

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