Identifying the contributory chains and patterns of road facilities in bus-involved crashes using latent class clustering and association rules

The proposed framework, which integrates LCC and ARM, enables a more nuanced analysis of bus-involved crash data by separating classification and association rules analysis based on controllability. The results provide actionable insights into how specific road infrastructure features contribute to different types and severities of crashes. This can inform the design of targeted safety measures and infrastructure improvements to reduce crash risks in urban bus systems. Moreover, the framework is adaptable to other transportation modes and crash datasets.