Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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An Analysis of Daily Maximum Traffic Accident Using Generalized Extreme Value Distribution

일반화 극단치분포를 이용한 일 최대 교통사고 분석

  • Kim, Junseok (Department of Statistics, Daegu University) ;
  • Kim, Daesung (Department of Statistics, Daegu University) ;
  • Yoon, Sanghoo (Division of Mathematics and Big Data Science, Daegu University)
  • 김준석 (대구대학교 일반대학원 통계학과) ;
  • 김대성 (대구대학교 일반대학원 통계학과) ;
  • 윤상후 (대구대학교 수리.빅데이터학부)
  • Received : 2020.07.24
  • Accepted : 2020.10.20
  • Published : 2020.10.28
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Abstract

In order to cope with traffic accidents efficiently, the maximum number of traffic accidents, deaths and serious injuries that can occur during the day should be presented quantitatively. In order to examine the characteristics of traffic accidents in different regions, it was divided into the Seoul metropolitan area, Chungcheong area, Gyeongbuk area, Honam area, and Gyeongnam area and was suitable for the generalized extreme value distribution (GEV). The parameters of the GEV distribution were estimated by the L-moments, and the Anderson-Darling test and the Cramer-von Mises test confirmed the suitability of the distribution. According to the analysis, the maximum number of traffic accidents that can occur once every 50 years is 401 in the Seoul metropolitan area, 168 in the South Gyeongsang region, 455 in the North Gyeongsang region, 136 in the Chungcheong region and 205 in the South Jeolla region. Compared to the Seoul metropolitan area, which has a large population and car registration, the number of traffic accidents is relatively high due to the large area, mountainous areas, and logistics movement caused by the industrial complex.

대형 교통사고는 많은 인명피해를 동반한다. 교통사고를 효율적으로 대처하기 위해선 하루 동안 발생할 수 있는 최대 교통사고 수와 사망자 수, 중상자 수가 정량적으로 제시되어야 한다. 본 연구는 교통사고분석시스템에서 제공하는 2005년부터 2018년까지 전국에서 발생한 일 최대 교통사고 수, 사망자 수, 중상자 수 자료를 사용하여 15년, 30년, 50년에 한 번 발생할 수 있는 최대값을 제시하고자 한다. 지역별 교통사고의 특성을 살펴보기 위해 수도권, 충청권, 경북권, 호남권, 경남권으로 구분하여 일반화극단치분포(GEV분포)에 적합시켰다. GEV분포의 모수는 L-적률추정법으로 추정하였고, Anderson Darling 검정과 Cramer-von Mises 검정으로 분포의 적합성을 확인하였다. 분석결과 50년에 한 번 발생할 수 있는 일 최대 교통사고 수는 수도권 401건, 경남권 168건, 경북권 455건, 충청권 136건, 호남권 205건이다. 인구수와 자동차 등록수가 많은 수도권에 비해 경북권은 면적이 넓고 산지지형이 많으며 산업공단으로 인한 물류이동이 많아 교통사고 수가 상대적으로 높게 나타났다.

Keywords

References

  1. S. H. Park, D. H. Kim & J. A. Park. (2020). The effect of urban tissue on pedestrian traffic accidents in the living roads : Focused on the pedestrian traffic accident hot spots section in the Seoul's living road. Journal of Korea Planning Association, 55(2), 5-14. DOI : 10.17208/jkpa.2020.04.55.2.5
  2. A. F. Jenkinson. (1955). The frequency distribution of the annual maximum (or minimum) values of meteorological elements. Quarterly Journal of the Royal Meteorological Society, 81(348), 158-171. DOI : 10.1002/qj.49708134804
  3. S. Ryu, E. Eom, T. Kwon & S. Yoon. (2016). The estimation of CO concentration in Daegu- Gyeongbuk area using GEV distribution. Journal of the Korean Data & Information Science Society, 27(4), 1001-1012. DOI : 10.7465/jkdi.2016.27.4.1001
  4. X. Liang. (2014). Return levels of climatic factors based on extreme value theory, Master's Thesis, Pusan national university, Busan.
  5. M. Yang & S. Yoon. (2017). Evaluation of the impact of typhoon on daily maximum precipitation. Journal of the Korean Data & Information Science Society, 28(6), 1415-1425. DOI : 10.7465/jkdi.2017.28.6.1415
  6. D. K. Koh, T. H. Choo, S. J. Maeng, and C. Trivedi. (2008). Regional frequency analysis for rainfall using L-moment. The Journal of the Korea Contents Association, 8, 252-263.
  7. S. Coles, J. Bawa, L. Trenner & P. Dorazio. (2001). An introduction to statistical modeling of extreme values (Vol. 208, p. 208). London: Springer.
  8. S. L. Kang, & C. H. Park. (2003). A GIS-based Traffic Accident Analysis on Highways using Alignment Related Risk Indices. Journal of Korean Society of Transportation, 21, 21-39.
  9. Y. Y. Kim, K. H. Cho, & Y. Kim. (2020). Analysis of risk factors for traffic accidents in Daegu area. Journal of the Korean Data And Information Science Society, 31(3), 503-510. DOI : 10.7465/jkdi.2020.31.3.503
  10. J. Kang & S. Lee. (2002). Traffic accident prediction model by freeway geometric types. Journal of Korean Society of Transportation, 20(4), 163-175.
  11. J. S. Park, T. Y. Kim, & D. S. Yu. (2007). Correlation Analysis and Estimation Modeling Between Road Environmental Factors and Traffic Accident. Journal of Korean Society of Transportation, 25(2), 63.
  12. S. R. Mun, Y. I. Lee, & S. B. Lee. (2012) Developing a Traffic Accident Prediction Model for Freeways. Journal of Korean Society of Transportation, 30(2), 101-116. DOI : 10.7470/jkst.2012.30.2.101
  13. H. Oh & S. Yoon. (2017). Generalized extreme value distribution for a drought based on inter-amount time. Journal of the Korean Data & Information Science Society, 30(3), 563-571 DOI : 10.7465/jkdi.2019.30.3.563
  14. J. Y. Shin, Y. J. Park & T. W. Kim. (2013). Estimation of future design rainfalls in administrative districts using nonstationary GEV model. Journal of KOSHAM, 13(3), 147-156. DOI : 10.9798/KOSHAM.2013.13.3.147
  15. J. R Hosking. (1990). L-moments: Analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society: Series B (Methodological), 52(1), 105-124. https://doi.org/10.1111/j.2517-6161.1990.tb01775.x
  16. J. A. Greenwood, J. M. Landwehr, N. C. Matalas & J. R. Wallis. (1979). Probability weighted moments: definition and relation to parameters of several distributions expressable in inverse form. Water resources research, 15(5), 1049-1054. https://doi.org/10.1029/WR015i005p01049
  17. T. W. Anderson & D. A. Darling. (1952). Asymptotic theory of certain" goodness of fit" criteria based on stochastic processes. The annals of mathematical statistics, 193-212.
  18. N. K. Ko, I. D. Ha & D. H. Jang. (2020). Comparison of log-logistic and generalized extreme value distributions for predicted return level of earthquake. The Korean Journal of Applied Statistics, 33(1), 107-114. DOI : 10.5351/KJAS.2020.33.1.107