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    This study thoroughly investigated factors affecting crash occurrence using detailed data of crash, traffic condition and freeway geometries. To fully account for heterogeneity induced by unobserved characteristics of crash factors, a mixed logit model with mean-variance heterogeneity was estimated as an alternative to the commonly used mixed logit model and the fixed parameters logit model. Results indicate that the mixed logit model with mean-variance heterogeneity could improve the goodness-of-fit and was more flexible in accounting for unobserved heterogeneity compared with its counterparts. Additionally, by allowing means and variances of random parameters to be estimable functions of explanatory variables, the safety effect of interactions among multiple factors was concluded, for example: (1) sharp curves resulted in an increasing risk of crash and the rate of increase was positively correlated with the distance travelled by vehicles along a steep downgrade; (2) the adverse safety effect of steep downgrade increased with the distance covered by vehicles, especially for segments with high proportion of heavy trucks; (3) downhill segments with steep slopes were particularly dangerous. Findings from this study are expected to provide an insightful knowledge to the mechanism of crash occurrence and should be beneficial to design and manage safer freeways.


    Xiaoyan Huo, Junqiang Leng, Lijun Luo, Dan Wang. A mixed logit model with mean-variance heterogeneity to investigate factors of crash occurrence. International journal of injury control and safety promotion. 2021 Sep;28(3):301-308

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    PMID: 34013845

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