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

  • 제21권5호
  • /
  • Pages.90-102
  • /
  • 2022
  • /
  • 1738-0774(pISSN)
  • /
  • 2384-1729(eISSN)
한국ITS학회 (The Korea Institute of Intelligent Transport Systems)
권호 표지
DOI QR코드

DOI QR Code

기계학습을 활용한 고령운전자 교통사고 분석 및 교통사고 데이터 정책 제언

Elderly Driver-involved Crash Analysis and Crash Data Policy

  • 김승훈 (국토연구원 국토인프라연구본부)
  • Kim, Seunghoon (National Infrastructure Research Division in Korea Research Institute for Human Settlements)
  • 투고 : 2022.07.22
  • 심사 : 2022.09.20
  • 발행 : 2022.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

우리나라가 고령화시대에 진입하면서 고령운전자를 위한 교통 안전성 정책에 대한 관심이 높아지고 있다. 이를 위해서는 고령자 관련 교통사고의 영향요인을 분석하는 연구가 활성화될 필요가 있지만, 국내의 사고 데이터는 효과적인 사고분석 연구를 수행하기에는 한계가 있다. 이에 본 연구는 미국의 사고 데이터를 살펴보고 기계학습 알고리즘을 활용하여 고령운전자 사고심각도 예측 모형을 개발하고, 주요 사고 영향요인을 도출하여, 향후 국내 사고 데이터의 보완 방향을 제시하고자 한다. 분석 결과에 따르면, 주행속도, 제한속도, 사고 시 근접 주행 여부 등이 고령운전자 사고 심각도에 영향을 주는 요인으로 나타났는데, 한국의 사고 데이터에서 제공하지 않는 것으로 나타났다. 그러므로 이와 같은 정보들이 한국의 사고 데이터에서 제공된다면 고령운전자 교통안전성 제고에 기여할 수 있을 것이다.

Currently, in our society with a substantial and increasing fraction of the elderly population, transport safety for elderly drivers is becoming the center of attention. However, deficient data on vehicle crashes in South Korea limits the growth of traffic accident research pertaining to the country. So, we complemented South Korean vehicle crash data by examining USA vehicle crash data, especially the data of Ohio State, and analyzing the influential factors of elderly driver-involved crashes of the State. Subsequently, we suggested a way of improving the South Korean dataset. Notably, our study showed that the influential factors were vehicle speed, posted speed, and following other vehicles too close and provided them in the South Korean dataset.

키워드

참고문헌

  1. Al Mamlook, R. E., Abdulhameed, T. Z., Hasan, R., Al-Shaikhli, H. I., Mohammed, I. and Tabatabai, S.(2020), "Utilizing Machine Learning Models to Predict the Car Crash Injury Severity among Elderly Drivers", 2020 IEEE International Conference on Electro Information Technology(EIT), pp.105-111.
  2. Ben-Akiva, M. E. and Lerman, S. R.(1985), Discrete Choice Analysis: Theory and Application to Travel Demand, Cambridge, MA: MIT Press.
  3. Boufous, S., Finch, C., Hayen, A. and Williamson, A.(2008), "The impact of environmental vehicle and driver characteristics on injury severity in older drivers hospitalized as a result of a traffic crash", Journal of Safety Research, vol. 39, no. 1, pp.65-72. https://doi.org/10.1016/j.jsr.2007.10.010
  4. Breiman, L.(2001), "Random forests", Machine Learning, vol. 45, pp.5-32. https://doi.org/10.1023/A:1010933404324
  5. Hakamies-Blomqvist, L. and Henriksson, P.(1999), "Cohort effect in older drivers' accident type distribution: Are older drivers as old they used to be?", Transportation Research Part F, vol. 2, no. 3, pp.131-138. https://doi.org/10.1016/S1369-8478(99)00009-1
  6. Jang, J., Choi, J. and Gim, T.(2017), "Analyzing Driving Environment Effects on Severity of Elderly Driver's Traffic Accidents", Journal of Transport Research, vol. 24, no. 1, pp.79-94. https://doi.org/10.34143/jtr.2017.24.1.79
  7. Korea Road Traffic Authority Traffic Science Institute(2015), A Study on the Major factor of High-risk Driver Groups' Accidents: Focusing on Elderly Drivers, pp.1-85.
  8. Lechner, M. and Okasa, G.(2019), Random Forest Estimation of the Ordered Choice Model, arXiv preprint (2022) Available online: https://arxiv.org/pdf/1907.02436.pdf, 2022.09.06.
  9. Lee, J. and Gim, T.(2019), "Examining the Characteristics of Traffic Accidents Involving Elderly", The Korea Spatial Planning Review, vol. 102, pp.19-34. https://doi.org/10.15793/KSPR.2019.102..002
  10. Lee, M. J. and Lee, M. S.(2014), "Elderly Driver's Perceived Driving Ability and Driving Behavior Associated with Traffic Accident Risk", Crisisonomy, vol. 10, no. 12, pp.279-304.
  11. Lee, S. C.(2006), "Psychological effects on elderly driver's traffic accidents", Korean Journal of Psychological and Social Issues, vol. 12, no. 5, pp.149-167.
  12. Lee, Y. T., Kim, M. H. and Son, J. W.(2009), "The impact of cognitive workload on older driver's behavior", The Korean Society of Automotive Engineers(KSAE), pp.982-987.
  13. Mafi, S., AbdelRazig, Y. and Doczy, R.(2018), "Machine Learning Methods to Analyze Injury Severity of Drivers from Different Age and Gender Groups," Transportation Research Record, vol. 2672, no. 38, pp.171-183.
  14. Mccullagh, P.(1980), "Regression Models for Ordinal Data", Journal of the Royal Statistical Society: Series B (Methodological), vol. 42, pp.109-127. https://doi.org/10.1111/j.2517-6161.1980.tb01109.x
  15. McFadden, D.(1973), Conditional logit analysis of qualitative choice behavior, University of California at Berkeley.
  16. Oh, J. S., Lee, E. Y., Ryu, J. B. and Lee, W. Y.(2015), "An Analysis for Main Vulnerable Situations and Human Errors of Elderly Drivers' Traffic Accidents", Journal of Transport Research, vol. 22, no. 4, pp.57-75. https://doi.org/10.34143/JTR.2015.22.4.57
  17. Ohio Department of Public Safety(ODPS) Crash Statistics System(2021), Available online: https://ohtrafficdata.dps.ohio.gov/CrashStatistics/Home, 2021.02.17.
  18. Preusser, D. F., Williams, A. F., Ferguson, S. A., Ulmer, R. G. and Weinstein H. B.(1998), "Fatal crash risk for older drivers at intersections", Accident Analysis and Prevention, vol. 30, no. 2, pp.151-159. https://doi.org/10.1016/S0001-4575(97)00090-0
  19. Yang, K. S., Kwon, S. M. and Youn, C. W.(2021), "An Analysis of the Determinants of Elderly Pedestrian Fatal Crash," Journal of the Korean Urban Management Association, vol. 34, no. 1, pp.21-33. https://doi.org/10.36700/KRUMA.2021.3.34.1.21
  20. Yu, J. H. and Choe, G. I.(2013), "A Comparative Analysis on Characteristics between Elderly Drivers and Younger Drivers by Accident Types: With Commercial Vehicles", Transportation Technology and Policy, vol. 10, no. 5, pp.11-25.
  21. Zhang, J., Lindsay, J., Clarke, K., Robbins, G. and Mao, Y.(2000), "Factors affecting the severity of motor vehicle traffic crashes involving elderly drivers in Ontario", Accident Analysis and Prevention, vol. 32, no. 1, pp.117-125. https://doi.org/10.1016/S0001-4575(99)00039-1