Temporal stability of driver injury severities in animal-vehicle collisions: A random parameters with heterogeneity in means (and variances) approach

• Published in: Analytic methods in accident research Vol. 26; p. 100120
• Main Authors: Al-Bdairi, Nabeel Saleem Saad, Behnood, Ali, Hernandez, Salvador
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2020
• Subjects: Animal-vehicle collision; Heterogeneity-in-means and variance; Random parameters; Unobserved heterogeneity; Animal-vehicle collision; Random parameters; Unobserved heterogeneity; Heterogeneity-in-means and variance
• ISSN: 2213-6657; 2213-6657
• DOI: 10.1016/j.amar.2020.100120

•Affecting factors on the injury severity of animal vehicle collisions are investigated.•Mixed logit models with heterogeneity in means and variances are used.•Accounting for heterogeneity in means and variances improves overall model fit.•Explanatory variables show relatively similar marginal effects across different methodological approaches.•Explanatory variables show temporally unstable behavior across different time periods. This study investigates the determinants of driver injury severity in animal-vehicle collisions while systematically accounting for unobserved heterogeneity in the data by using three methodological approaches: mixed logit model, mixed logit model with heterogeneity in means, and mixed logit model with heterogeneity in means and variances. Using the data from Washington state from January 1, 2012 to December 31, 2016, a wide range of factors that could potentially affect the injury severity of drivers were examined. Moreover, the temporal stability and transferability of the models were investigated through a series of likelihood ratio tests. Marginal effects were also used to study the temporal stability of the explanatory variables. Model estimation results show that many parameters can potentially increase the likelihood of severe injuries in Animal-vehicle crashes including freeways/expressways, daylight crashes, early morning crashes, dry road surface and clear weather condition. Moreover, the model estimation results show that accounting for the heterogeneity in the means (and variances) of the random parameters can improve the overall fit of the model. Some variables showed relatively similar marginal effects among different methodological approaches while some others showed different marginal effects upon the application of different methods. With regard to temporal stability of explanatory variables, the findings of this study show how underestimating the temporal stability concept may lead to inaccurate and unreliable conclusions.