Investigating influential factors through crash frequency models considering excess zeros and heterogeneity: New insights into mountain freeway safety

The use of statistical modeling methods to quantify crash causation on mountain freeways is limited by crash data availability and technical challenges posed by excess zeros and heterogeneity, resulting in a lack of significant targeting of proactive crash prevention measures on mountain freeways. We collected multidimensional crash-related information on mountainous freeways in China, including road design characteristics, traffic conditions, pavement performance, and weather conditions. To overcome the challenges of excess zeros and heterogeneity on modeling techniques, we innovatively developed two new models: the Random Parameter Negative Binomial Lindley (RPNB-L) model and the Random Parameter Negative Binomial Generalized Exponential (RPNB-GE) model. The goodness-of-fit indicates that the RPNB-L and RPNB-GE models stand out among the six competing models, suggesting that the Lindley and GE distributions are conducive to portraying the multi-zero attributes, while the regression coefficients randomization treatment provides a deeper portrayal of heterogeneous effects. Moreover, the analysis reveals a considerable number of causes for crash frequency on mountainous freeways in China. These include several interesting results, such as special segments like tunnels and interchanges, Pavement Damage Condition Index (PCI) and the stormy rainfall (TR), which have not been extensively studied in previous research. The research results provided important reference values for the selection of active safety countermeasures for mountain freeways.