Exploring macroscopic factors contributing to provincial road safety performance and rank transitions in China using association rule mining

Addressing macroscopic road safety complexities requires understanding regional factor interactions. To bridge static performance benchmarking and dynamic causation analysis, this study proposes a two-stage “Performance Evaluation—Driving Factor Diagnosis” framework. Utilizing 2023–2024 panel data exclusively from the National Bureau of Statistics for 30 Chinese provinces, safety indicators are decoupled into an Evaluation Level and a Diagnostic Level. Performance is objectively stratified using a multicriteria pipeline (MEREC, PROMETHEE II, and K-Means). Subsequently, Apriori association rule mining extracts multidimensional associative configurations from both static and dynamic perspectives. Results reveal significant systemic inertia, with 76.67% of provinces maintaining stable safety ranks. Static mining demonstrates explicit functional pathways: physical infrastructure primarily suppresses accident frequency, whereas robust medical resources serve as a critical containment mechanism against severe casualties. Dynamically, upward rank mobility requires the synergistic expansion of both road infrastructure and medical capacity. Conversely, rank declines are precipitated by resource-risk mismatches, such as medical personnel deficits relative to escalating traffic exposure. Ultimately, this research provides an evidence-based approach for precision safety governance, transitioning from generic interventions to targeted capacity building tailored to regional structural characteristics.