A Bayesian Bivariate Random Parameters and Spatial-Temporal Negative Binomial Lindley Model for Jointly Modeling Crash Frequency by Severity: Investigation for Chinese Freeway Tunnel Safety

Abstract

As hazardous locations of a road, freeway tunnels have a higher risk of casualty than open roads. Therefore, it is necessary to seek a reliable crash prediction model and propose targeted improvement measures. However, existing studies on freeway tunnel crash models mainly suffer from the following problems: 1) They ignore the correlation between different injury severity levels of crashes; 2) They ignore the impact of excess zero observations; 3) They do not consider the influence of heterogeneity between samples and the spatio-temporal correlation. To solve the above problems, this paper has compiled a dataset with freeway tunnel design features, three years of traffic conditions, pavement conditions, and traffic crash data. Then, a bivariate random parameters negative binomial Lindley model (ST-BRPNB-L) is established for jointly modeling crash counts and injury severity levels, which consider excess zero observations by introducing Lindley parameters, characterize the heterogeneity, and spatial-temporal correlation between samples by introducing random parameters and spatio-temporal parameters. The Bayesian estimation results have shown that ST-BRPNB-L has the best goodness-of-fit among a series of comparison models, which verifies the superiority of the proposed model. On this basis, the influence of the risk factors on the frequency and severity of crashes was quantitatively analyzed based on the ST-BRPNB-L model’s parameters estimation results, which provides a scientific basis for safety improvement measures of freeway tunnels.