대한교통학회지 (Journal of Korean Society of Transportation)

  • 제25권5호
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  • Pages.135-148
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  • 2007
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  • 1229-1366(pISSN)
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  • 2234-4217(eISSN)
대한교통학회 (Korean Society of Transportation)
권호 표지

AFT 생존분석 기법을 이용한 고속도로 교통사고 지속시간 예측모형

A Prediction Model on Freeway Accident Duration using AFT Survival Analysis

  • 발행 : 2007.10.30
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초록

교통사고의 특성과 사고에 대한 지속시간 사이의 관계에 대한 이해는 사고의 효과적인 대응과 사고에 의한 혼잡을 감소시키는데 핵심 요소가 된다. 때문에 본 연구의 목적은 AFT metric 모형을 적용한 사고 지속시간을 분석하는 것이다. 비록 로그 로지스틱 및 로그 정규 AFT 모형이 통계적 이론과 기존 연구 사례를 기반으로 선정되었으나, 로그 로지스틱 모형이 보다 우수하게 추정되었다. AFT 모형은 예측 목적으로도 널리 사용되기 때문에, 추정된 모형은 사고 발생시 사고 관련 기본 정보 접수 즉시 고속도에서의 사고 지속시간 예측에 사용될 수 있다. 결과적으로, 예측된 사고 지속시간 정보는 사고를 처리하기 위한 제반 의사 결정에 도움을 줄 뿐 아니라 교통 혼잡의 감소 및 추가 사상자의 감소로 그 효과가 이어질 것으로 판단된다.

Understanding the relation between characteristics of an accident and its duration is crucial for the efficient response of accidents and the reduction of total delay caused by accidents. Thus the objective of this study is to model accident duration using an AFT metric model. Although the log-logistic and log-normal AFT models were selected based on the previous studies and statistical theory, the log-logistic model was better fitted. Since the AFT model is commonly used for the purpose of prediction, the estimated model can be also used for the prediction of duration on freeways as soon as the base accident information is reported. Therefore, the predicted information will be directly useful to make some decisions regarding the resources needed to clear accident and dispatch crews as well as will lead to less traffic congestion and much saving the injured.

키워드

참고문헌

  1. Akaike, H. (1974), 'A new look at the statistical model identification', IEEE Transactions on Automatic Control, 19(6), pp.716-723 https://doi.org/10.1109/TAC.1974.1100705
  2. Bendel, R. B. and A. A. Afifi (1977), 'Comparison of Stopping Rules in Forward 'Stepwise' Regression', Journal of the American Statistical Association, 72(357), pp.46-53 https://doi.org/10.2307/2286904
  3. Cantor, A. (2003), 'SAS survival analysis techniques for medical research', Cary, NC, SAS Institute
  4. Collett, D. (2003), 'Modelling survival data in medical research', Boca Raton, FL, Chapman & Hall/CRC
  5. Costanza, M. C. and A. A. Afifi (1979), 'Comparison of Stopping Rules in Forward Stepwise Discriminant Analysis' Journal of the American Statistical Association, 74(368), pp.777-785 https://doi.org/10.2307/2286399
  6. Cox, D. R. (1972), 'Regression Models and Life Tables', Journal of the Royal Statistical Society, Series B (Methodological), 34(2), pp.187-220
  7. De Rose Jr., F. (1964), 'An analysis of random freeway traffic accidents and vehicle disabilities', Highway Research Record, 59, pp.53-65
  8. Fisher, B., S. Anderson, et al. (1991), 'Significance of ipsilateral breast tumour recurrence after lumpectomy', The Lancet, 338(8763), pp.327- 331 https://doi.org/10.1016/0140-6736(91)90475-5
  9. Garib, A., A. E. Radwan, et al. (1997), 'Estimating Magnitude and Duration of Incident Delays', Journal of Transportation Engineering, 123(6), pp.459-466 https://doi.org/10.1061/(ASCE)0733-947X(1997)123:6(459)
  10. Giuliano, G. (1989), 'Incident characteristics, frequency, and duration on a high volume urban freeway', Transportation Research Part A: General, 23(5), pp.387-396 https://doi.org/10.1016/0191-2607(89)90086-1
  11. Golob, T. F., W. W. Recker, et al. (1987), 'An analysis of the severity and incident duration of truck‐involved freeway accidents', Accident Analysis & Prevention, 19(5), pp.375-395 https://doi.org/10.1016/0001-4575(87)90023-6
  12. Goolsby, M. E., W. Smith, et al. (1971), 'Influence of incidents on freeway quality of service', Highway Research Record, 349, pp.41-46
  13. Hosmer, D. W. and S. Lemeshow (1999), 'Applied survival analysis: regression modeling of time to event data', New York, Wiley
  14. Jones, B., L. Janssen, et al. (1991), 'Analysis of the frequency and duration of freeway accidents in Seattle', Accident Analysis & Prevention, 23(4), pp.239-255 https://doi.org/10.1016/0001-4575(91)90003-N
  15. Juge, J., K. Kennedy, et al. (1974), 'Early detection and rapid removal of disabled vehicles and other hazards from the freeway', California Department of Transportation and Department of California Highway Patrol
  16. Khattak, A., J. Schofer, et al. (1995), 'A Simple Procedure for Predicting Freeway Incident Duration', IVHS Journal, 2(2), pp.113-138
  17. Nam, D. and F. Mannering (2000), 'An exploratory hazard‐based analysis of highway incident duration', Transportation Research Part A: Policy and Practice, 34(2), pp.85-102 https://doi.org/10.1016/S0965-8564(98)00065-2
  18. Ozbay, K. and P. Kachroo (1999), 'Incident management in intelligent transportation systems', Boston, Artech House
  19. Royston, P. and D. G. Altman (1994), 'Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling', Applied Statistics, 43(3), pp.429-467 https://doi.org/10.2307/2986270
  20. Smith, K. and B. L. Smith (2001), 'Forecasting the Clearance Time of Freeway Accidents', Smart Travel Lab Report. Charlottesville, VA, Center for Transportation Studies (University of Virginia)
  21. Stathopoulos, A. and M. G. Karlaftis (2002), 'Modeling Duration of Urban Traffic Congestion', Journal of Transportation Engineering, 128(6), pp.587-590 https://doi.org/10.1061/(ASCE)0733-947X(2002)128:6(587)
  22. TRB (1994), 'Highway capacity manual: special report 209', Washington, D. C., Transportation Research Board, National Research Council
  23. Vaupel, J. W., K. G. Manton, et al. (1979), 'The Impact of Heterogeneity in Individual Frailty on the Dynamics of Mortality', Demography, 16(3), pp.439-454 https://doi.org/10.2307/2061224
  24. 백승걸.박재범 (2004), '고속도로 돌발상황으로 인한 교통영향 예측시스템 고찰', 도로교통, 94(겨울), pp.30-40