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A Prediction Model on Freeway Accident Duration using AFT Survival Analysis  

Jeong, Yeon-Sik (한국교통연구원)
Song, Sang-Gyu (한국도로공사 SmartWay사업단)
Choe, Gi-Ju (아주대학교 환경건설교통공학부)
Publication Information
Journal of Korean Society of Transportation / v.25, no.5, 2007 , pp. 135-148 More about this Journal
Abstract
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.
Keywords
Accident Duration; Survival Analysis; AFT Model; Fractional Polynomials; AIC(Akaike'S Information Criterion;
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  • Reference
1 Collett, D. (2003), 'Modelling survival data in medical research', Boca Raton, FL, Chapman & Hall/CRC
2 Fisher, B., S. Anderson, et al. (1991), 'Significance of ipsilateral breast tumour recurrence after lumpectomy', The Lancet, 338(8763), pp.327- 331   DOI   ScienceOn
3 Goolsby, M. E., W. Smith, et al. (1971), 'Influence of incidents on freeway quality of service', Highway Research Record, 349, pp.41-46
4 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   DOI   ScienceOn
5 Khattak, A., J. Schofer, et al. (1995), 'A Simple Procedure for Predicting Freeway Incident Duration', IVHS Journal, 2(2), pp.113-138
6 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   DOI   ScienceOn
7 Stathopoulos, A. and M. G. Karlaftis (2002), 'Modeling Duration of Urban Traffic Congestion', Journal of Transportation Engineering, 128(6), pp.587-590   DOI   ScienceOn
8 Garib, A., A. E. Radwan, et al. (1997), 'Estimating Magnitude and Duration of Incident Delays', Journal of Transportation Engineering, 123(6), pp.459-466   DOI   ScienceOn
9 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   DOI   ScienceOn
10 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   DOI
11 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)
12 Cox, D. R. (1972), 'Regression Models and Life Tables', Journal of the Royal Statistical Society, Series B (Methodological), 34(2), pp.187-220
13 Hosmer, D. W. and S. Lemeshow (1999), 'Applied survival analysis: regression modeling of time to event data', New York, Wiley
14 TRB (1994), 'Highway capacity manual: special report 209', Washington, D. C., Transportation Research Board, National Research Council
15 Akaike, H. (1974), 'A new look at the statistical model identification', IEEE Transactions on Automatic Control, 19(6), pp.716-723   DOI
16 Royston, P. and D. G. Altman (1994), 'Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling', Applied Statistics, 43(3), pp.429-467   DOI   ScienceOn
17 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   DOI
18 백승걸.박재범 (2004), '고속도로 돌발상황으로 인한 교통영향 예측시스템 고찰', 도로교통, 94(겨울), pp.30-40
19 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   DOI
20 Cantor, A. (2003), 'SAS survival analysis techniques for medical research', Cary, NC, SAS Institute
21 De Rose Jr., F. (1964), 'An analysis of random freeway traffic accidents and vehicle disabilities', Highway Research Record, 59, pp.53-65
22 Giuliano, G. (1989), 'Incident characteristics, frequency, and duration on a high volume urban freeway', Transportation Research Part A: General, 23(5), pp.387-396   DOI   ScienceOn
23 Ozbay, K. and P. Kachroo (1999), 'Incident management in intelligent transportation systems', Boston, Artech House
24 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