Causal factors identification and dynamics simulation of major road traffic accidents from China’s evidence: A high-order mixed-method design

Abstract

Mitigating the injury and severity of road traffic accidents has become a crucial objective in global road safety efforts. Major road traffic accidents (MRTAs) pose significant challenges due to their high hazard and severe consequences. Despite their widespread impact, the complex causation mechanisms behind MRTAs have not been thoroughly and systematically investigated, which hinders the development of effective control strategies and policies. This study introduces an innovative high-order embedded mixed-method design to explore the causes of MRTAs, marking the first application of mixed-method approaches in road traffic accident research. The proposed approach consists of three phases: First, qualitative analysis utilizing grounded theory examines 95 MRTAs investigation reports to identify causal factors, establish a classification framework, and derive quantitative data. The second phase employs the decision experiment and evaluation laboratory (DEMATEL) for static quantitative analysis, quantifying interactions within the classification framework, and generating cause-effect diagrams. Finally, data and results from the first two phases are integrated to construct a system dynamics (SD) model and conduct sensitivity analysis, analyzing the impact of causal factors and their interactions on MRTAs casualties, thereby evaluating the effectiveness of various control strategies. The findings reveal that the causal factors of MRTAs can be categorized into five levels: “driver errors,” “vehicle, road and environment,” “supervisory deficiencies,” “organizational management and culture,” and “outside factors.” Complex interactions exist both among and within these levels, collectively influencing MRTAs. Moreover, in reducing MRTAs casualties, combined control strategies demonstrate significant superiority over single control strategies, especially when targeting key factors. It should also be noted that the importance ranking of causal factors dynamically adjusts with changes in the control environment, and the effectiveness of combined control strategies becomes more pronounced as the number of control factors increases. Specifically, comprehensive prevention strategies across all five levels exhibit the most remarkable efficacy. In conclusion, preventing MRTAs requires emphasizing the shared responsibility of all stakeholders and judiciously allocating control resources, while avoiding excessive reliance on interventions targeting any specific factor. This study provides a methodological foundation for a deeper understanding of the causation mechanisms behind MRTAs, and its results offer robust evidence to support the formulation of future prevention measures and policies.