Analysis of accident injury-severities using a correlated random parameters ordered probit approach with time variant covariates
By Grigorios Fountas, Panagiotis Ch. Anastasopoulos, Mohamed A. Abdel-Aty. Analytic Methods in Accident Research, 2018
227 citations recorded by the source corpus.
Abstract This paper employs a correlated random parameters ordered probit modeling framework to explore time-variant and time-invariant factors affecting injury-severity outcomes in single-vehicle accidents. The proposed approach extends traditional random parameters modeling, by accounting for possible correlations among the random parameters. On the basis of an unrestricted covariance matrix for the random parameters, the proposed framework can capture the combined effect of the unobserved factors – which are captured by the random parameters – on the injury-severity mechanism. The empirical analysis is based on traditional roadway-, traffic- and crash-specific information, and detailed weather and pavement surface disaggregate data, collected in the State of Washington, between 2011 and 2013. The results show that accident injury-severity outcomes are affected by a number of time-variant (ice thickness or water depth on pavement surface, sub-surface temperature) and time-invariant (roadway geometrics, and vehicle-, driver-, and collision-specific characteristics) factors, several of which result in statistically significant parameters – thus they have mixed effects on the injury-severity generation mechanism. The findings also present statistically significant correlation effects among the random parameters, which substantiates the appropriateness of the approach. The comparative assessment between the employed approach and its lower-order counterparts (i.e., fixed parameters, and uncorrelated random parameters ordered probit modeling approaches) shows that accounting for the unobserved heterogeneity interactions results not only in superior statistical performance (in terms of model’s fit, and explanatory and prediction performance) but also in less biased and more consistent parameter estimates.
Subfields: Environmental Engineering, Public Health, Environmental and Occupational Health, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/e0c63d26e41ae64358019537d638f8b1b14cdbc7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
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): “…Bayesian random parameters models, grouped 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…”
- Analysis of accident injury-severity outcomes: The zero-inflated hierarchical ordered probit model with correlated disturbances (2018, methodology): “…previous studies (Eluru et al., 2008; Xiong et al., 2014; Yasmin et al., 2016; Fountas and Anastasopoulos, 2017; Sarwar and Anastasopoulos, 2017; Fountas et al., 2018b), the annual average daily traffic, the traffic control indicator, the County indicator, and the truck involvement indicator…”
- Mobility-on-demand versus fixed-route transit systems: An evaluation of traveler preferences in low-income communities (2019, methodology): “A major limitation of the analysis present here is the lack of consideration for unobserved heterogeneity, to address which researchers have made many methodological improvements to the ordered logit model applied in this study (Anowar et al., 2014; Fountas et al., 2018; Fountas et al., 2018).”