한국정보전자통신기술학회논문지 (The Journal of Korea Institute of Information, Electronics, and Communication Technology)

  • 제9권2호
  • /
  • Pages.243-247
  • /
  • 2016
  • /
  • 2005-081X(pISSN)
  • /
  • 2288-9302(eISSN)
한국정보전자통신기술학회 (Korea Information Electronic Communication Technology)
권호 표지
DOI QR코드

DOI QR Code

운전자 행동자료 및 고위험군 군집 분석

Drivers Driving Habits Data and Risk Group Cluster Analysis

  • Kim, Yong-Chul (Department of Logistics and Statistical Information, YongIn University)
  • 투고 : 2016.04.04
  • 심사 : 2016.04.21
  • 발행 : 2016.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은 급가속, 급 감속, 급제동, 급출발, 그리고 과속 등과 같은 여러 운행 이벤트 데이터는 운전자의 운행습관과 운전자의 사고위험성을 예측 또는 분석하는데 중요한 정보를 제공한다. 일반적인 자료의 분포는 정규분포, 로그정규분포, 감마분포 등을 이용하지만 운전자 운행습관을 나타내는 자료에서 사고위험성을 추정 할 수 있는 극단적인 부분에서는 언급한 분포로 적합하지 않은 경우가 발생한다. 특히 왜도가 발생하여 정규분포에 적합하지 않은 영역이 생겨난다. 본 논문에서는 이 영역에서 적합한 분포 함수와 사고를 유발하는 위험군을 분리 할 수 있고 운전자 운행 시 사전경고로 사고율을 줄 일수 있는 임계점을 분포함수의 quantile값과 군집분석 결과와 비교하여 제시하였다.

Driving Event Data such as the rapid acceleration, the rapid deceleration, the sudden braking, and the sudden departure, and over speeding provide important information to predict or analyze the driving habits and accident risk of a driver. Most of the data that represent the driver's driving habits generally fit to the parametric distribution, whereas extreme parts of the data to estimate the accident risk of a driver may not. This paper presents an empirical distribution that is divided into two regions, one is from the normal distribution, and the other is from the general pareto distribution for the driving habits of a driver.

키워드

참고문헌

  1. Yiilbyeong, imheonyeon, "A Study on traffic accidents prediction model developed in Korea," Korean Society of Transportation, Vol. 8, No. 1, pp. 73-88, 1990.
  2. Oeo, Gee Young, Do-Gyeong Kim, and Yuhwa Lee. "The Characteristics of Secondary Crashes Occurred on Expressways in Korea." International Journal of Highway Engineering 15.2 (2013): 139-147. https://doi.org/10.7855/IJHE.2013.15.2.139
  3. Jeongilyoung, "traffic accident types and causes safety breach Analysis", pp. 1-107, Transportation Safety Authority, 2012.
  4. Sonsoyoung, "traffic accident statistics analysis study," Statistical Analysis, Vol. 2, No. 2, pp. 181-201, 1997.
  5. Johnson, R.A and Wichern, D.W., "Applied Multivariate Statistical Analysis", , pp. 1-400. 1992.
  6. Masaru Ueyama and Hideo Chikasue and Kizuki Muramatu, RELATIONSHIP BETWEEN DRIVING BEHAVIOR AND TRAFFIC ACCIDENTS -ACCIDENT DATA RECORDER AND DRIVING MONITOR RECORDER", Paper Number 98-S2-O-06), 1998.
  7. Schmidt-Cotta, R R, "Vehicle Event Recording based on Intelligent Crash Assessment: VERONICA-II", pp. 1-203, 2009.
  8. Shanker, V. and Mannering, F., "An Exploratory Multiomial Logit Analysis of Single- Vehicle Motorcycle Accident Severity", Journal of Safety Research, Vol. 27. No.3. pp.183-194, 1996. https://doi.org/10.1016/0022-4375(96)00010-2
  9. Yasuhiro Yamai and Toshinao Yoshiba, "Comparitive analyses of expected shortfall and value-at-risk under market stress", IMES Discussion Paper No 2002-E-2,Bank of Japan, 2002.
  10. Yay M., Madrid N.M., Ramirez J.A.O., "Using an improved rule match algorithm in expert system to detect broken driving rules for an e4nergy-efficiency and safety relevant driving system", Procedia Coumputer Science 35, pp. 127-136, 2014. https://doi.org/10.1016/j.procs.2014.08.092