Analysis of accident injury-severity outcomes: The zero-inflated hierarchical ordered probit model with correlated disturbances
By Grigorios Fountas, Panagiotis Ch. Anastasopoulos. Analytic Methods in Accident Research, 2018
98 citations recorded by the source corpus.
Abstract In accident injury-severity analysis, an inherent limitation of the traditional ordered probit approach arises from the a priori consideration of a homogeneous source for the accidents that result in a no-injury (or zero-injury) outcome. Conceptually, no-injury accidents may be subject to the effect of two underlying injury-severity states, which are more likely to be observed in accident datasets with excessive amounts of no-injury accident observations. To account for this possibility along with the possibility of heterogeneity stemming from the fixed nature of the ordered probability thresholds, a zero-inflated hierarchical ordered probit approach with correlated disturbances is employed, for the first time – to the authors’ knowledge – in accident research. The latter consists of a binary probit and an ordered probit component that are simultaneously modeled in order to identify the influential factors for each underlying injury-severity state. At the same time, the model formulation accounts for possible correlation between the disturbance terms of the two model components, and allows for the ordered thresholds to vary as a function of threshold-specific explanatory variables. Using injury-severity data from single-vehicle accidents that occurred in the State of Washington, from 2011 to 2013, the implementation potential of the proposed approach is demonstrated. The comparative assessment between the zero-inflated hierarchical ordered probit approach with correlated disturbances and its lower-order counterparts highlights the potential of the proposed approach to account for the effect of underlying states on injury-severity outcome probabilities and to explain more with the same amount of information.
Subfields: Management, Monitoring, Policy and Law, Public Health, Environmental and Occupational Health, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/17f15b13b279089dfa20846d2f11d1d0e8535acc
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In Management, Monitoring, Policy and Law
In Public Health, Environmental and Occupational Health
In Safety, Risk, Reliability and Quality
Citations in the corpus
- Temporal stability of driver injury severities in animal-vehicle collisions: A random parameters with heterogeneity in means (and variances) approach (2020, methodology): “…latent class models with class probability functions and others (Boggs et al., 2020; Eker et al., 2019; Fountas et al., 2019, 2018b, 2018a; Fountas and Anastasopoulos, 2018, 2017; Heydari 7 et al., 2018; Huang et al., 2019; Marcoux et al., 2018; Shaheed et al., 2016; Venkataraman et…”