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Injury severity analysis with variable and correlated thresholds : Ordered mixed logit formulation Srinivasan, Karthik K

By: Srinivasan, Karthik KPublication details: Transportation Research Record, 2002Description: nr 1784, s. 132-42Subject(s): USA | Mathematical model | Injury | | Calibration | Accident | Characteristics | 812Bibl.nr: VTI P8169:2002 RefLocation: Abstract: An ordered mixed logit (OML) formulation is proposed to model injury severity, given a crash. The proposed formulation extends the ordered probit/logit models by accommodating variable, random, and correlated injury severity thresholds associated with various severity levels. The proposed model is calibrated using a sample from the 1996 National Automotive Sampling System General Estimates System data set. Chi-square tests indicate that the more general OML formulation provides a statistically superior representation of observed injury severity data that corresponding ordered logit models. Model results indicate that injury severity thresholds vary systematically depending on individual, traffic, crash-related, and vehicle characteristics. Further, significant unobserved variability in thresholds is found, and the thresholds are correlated within a given individual. The results suggest drastically increased chances of fatal injury due to certain factors including tripped rollovers and injuries sustained by moped riders. These findings suggest that targeting these drivers, behaviors, and conditions with suitable countermeasures including education, enforcement, or curfew is likely to result in substantial safety benefits. The model and results have important implications for developing effective safety countermeasures and more accurate assessment of their impacts.
Item type: Reports, conferences, monographs
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An ordered mixed logit (OML) formulation is proposed to model injury severity, given a crash. The proposed formulation extends the ordered probit/logit models by accommodating variable, random, and correlated injury severity thresholds associated with various severity levels. The proposed model is calibrated using a sample from the 1996 National Automotive Sampling System General Estimates System data set. Chi-square tests indicate that the more general OML formulation provides a statistically superior representation of observed injury severity data that corresponding ordered logit models. Model results indicate that injury severity thresholds vary systematically depending on individual, traffic, crash-related, and vehicle characteristics. Further, significant unobserved variability in thresholds is found, and the thresholds are correlated within a given individual. The results suggest drastically increased chances of fatal injury due to certain factors including tripped rollovers and injuries sustained by moped riders. These findings suggest that targeting these drivers, behaviors, and conditions with suitable countermeasures including education, enforcement, or curfew is likely to result in substantial safety benefits. The model and results have important implications for developing effective safety countermeasures and more accurate assessment of their impacts.

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