Examining the Interactive Moderating Effect of Factors Contributing to the Severity of Freeway Accidents

Freeway accidents, compared to ordinary highway accidents, are associated with more serious and even fatal injuries, thus warranting in-depth analysis. While previous studies have primarily examined the relationship between accident severity and contributing factors, few have systematically investigated their potential interactive and moderating effects, leaving the combined influence of multiple factors underexplored, particularly how they may amplify or mitigate each other’s effects. This study utilizes accident data from the Hebei Province section of the Beijing-Harbin Expressway in China (2017–2022), classifying accidents into three severity levels: light, medium, and severe. Using a four-model hierarchical linear modeling (HLM) approach, an analysis was conducted of how human factors, driver-vehicle factors, roadway factors, and environment-related factors influence accident severity. Results demonstrate that the random intercept and random slope with interaction of HLM provides the optimal fit. Based on the estimation coefficients, the variables with significant effects and the moderating effect paths are identified. The main finding is that the variables, including driver’s gender, vehicle type, time of day, weather, combinations of road alignment, and deflections of horizontal curves, show positive effects on the severity of traffic accidents, while driver’s age, driver’s driving experience, radius of horizontal curves, and radius of vertical curves show negative effects. Furthermore, roadway factors significantly moderated the effects of the other three factor categories through both strengthening and weakening mechanisms. These findings are expected to provide actionable insights for government agencies and policymakers to develop targeted strategies for reducing freeway accident severity.