Unobserved heterogeneity is a key factor leading to biased estimates and unreliable crash predictions, undermining the effectiveness of safety countermeasures. Two common options to address it are separating crash types and leveraging advanced statistical models. While finite mixture models have recently demonstrated superiority in this regard, they fail to account for correlations between different crash types. This study thus integrates the two approaches by developing bivariate finite mixture (BFM) models while decomposing crashes into single‐vehicle (SV) and multivehicle (MV) crashes so as to simultaneously address the correlation feature and unobserved heterogeneity. Two variants of the BFM model are developed with SV and MV crash counts as the bivariate response variable. The first variant, BFM‐I‐2, measures the correlation using multivariate normally distributed random error terms, while the second, BFM‐II‐2, employs a joint probability density function. Both bivariate models categorize SV and MV crashes into two subgroups, respectively. Among these models, BFM‐I‐2 exhibits better performance than BFM‐II‐2; it not only verifies the positive correlations between SV and MV crashes but also highlights substantial differences in the key contributing factors across the two crash types. Traffic volume exerts opposing effects on the two subgroups of SV crashes, indicating that unobserved heterogeneity may explain the inconsistent findings regarding its influence in previous research. For MV crashes, the two subgroups share few common risk factors: those in Component 1 are predominantly associated with traffic flow conditions, whereas Component 2 is largely affected by road geometry. These findings can support developing tailored freeway safety interventions.

As the primary collision patterns on China’s freeways, in-depth exploration of the influencing factors of rear-end and sideswipe crashes has long been crucial for implementing targeted countermeasures. However, the structural and traffic flow continuity of freeways endows crash data with complex spatial and site correlations, which—if neglected in safety modeling—can lead to biased estimates of safety effects. To address this issue, this study develops a bivariate conditional autoregressive (CAR) model based on the Poisson lognormal (PLN) distribution: the copula joint distribution captures the site correlation between the two crash types, while the CAR model addresses spatial correlations across adjacent segments. Compared with univariate models and bivariate models with jointly distributed residuals, the proposed model achieves optimal performance and reveals the distinct determinants of the two crash types. For instance, factors such as spiral curve length and mean speed affect both crash types but to varying degrees, while continuous horizontal curve length only influences rear-end crashes. The proposed model and the identified influencing factors provide significant support for engineers to improve road safety.