Analyzing road crash frequencies with uncorrelated and correlated random-parameters count models: An empirical assessment of multilane highways

yy Recent literature on highway safety research has focused on methodological advances to minimize misspecifications and the potential for erroneous estimates and invalid statistical inferences. To further these efforts, this study carries out an empirical assessment of uncorrelated and correlated random-parameters count models for analyzing road crash frequencies on multilane highways considering two crash severities; injury and no-injury. The empirical results indicate that the relative statistical performance of these models is comparable; however, the correlated random-parameters approach accounts for both the heterogeneous effects of explanatory factors across the road segments and the cross-correlations among the random-parameter estimates. As noted in the results, statistically significant correlation effects among the random parameters confirm the adequacy of this approach. The safety models for multilane highways presented in this study can be useful in (i) the detection of critical risk factors on these road types, (ii) the assessment of crash reduction due to improvements in pavement condition and retrofitting of roadway geometric features and, (iii) the prediction of crash frequency while comparing different design alternatives. As such, the outcomes of this study may assist design engineers and highway agencies in designing new or calibrating existing multilane highways from a safety standpoint. (C) 2019 Elsevier Ltd. All rights reserved.