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• W2034306099 abstract "The extant traffic safety research literature includes numerous examples of studies that assess those factors affecting the degree of injury sustained by crash-involved motor vehicle occupants. One important methodological concern in such work is the potential correlation in injury outcomes among occupants involved in the same crash, which may be due to common unobserved factors affecting such occupants. A second concern is unobserved heterogeneity, which is reflective of parameter effects that vary across individuals and crashes. To address these concerns, a random parameters bivariate ordered probit model is estimated to examine factors affecting the degree of injury sustained by drivers involved in angle collisions. The modeling framework distinguishes between the effects of relevant factors on the injury outcomes of the at-fault and not-at-fault parties. The methodological approach allows for consideration of within-crash correlation, as well as unobserved heterogeneity, and results in significantly improved fit as compared to a series of independent models with fixed parameters. While the factors affecting injury severity are found to be similar for both drivers, the magnitudes of these effects vary between the at-fault and not-at-fault drivers. The results demonstrate that injury severity outcomes are correlated for drivers involved in the same crash. Further, the impacts of specific factors may be over- or under-estimated if such correlation is not accounted for explicitly as a part of the analysis. Various factors are found to affect driver injury severity and the random parameters framework shows these effects to vary across crashes and individuals. The analytical approach utilized provides a useful framework for injury severity analysis." @default.
• W2034306099 created "2016-06-24" @default.
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• W2034306099 date "2014-04-01" @default.
• W2034306099 modified "2023-10-16" @default.
• W2034306099 title "Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model" @default.
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• W2034306099 doi "https://doi.org/10.1016/j.amar.2014.03.001" @default.
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