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The government plays an important role in road safety. However, the effectiveness of the government in the context of road traffic accidents (RTAs) is rarely measured quantitatively. This study aims to quantitatively examine the effects of government regulation on human and organizational factors. A contributing factors classification framework of RTAs is presented based on the human factors analysis and classification system, one of the most popular systems approaches. A total of 405 major RTAs was collected over a 20-year period (1997–2017) in China and analyzed through the structural equation model. The results lead to two main conclusions: the frequency of inadequate regulation, which has reached 343, is the highest frequency among all contributing factors; government regulation exhibits significant effects on organizational influences, unsafe supervision and unsafe behaviors. These findings provide a new perspective for accident prevention that can be initiated by the government in policy-making and regulatory activities. [ABSTRACT FROM AUTHOR]

Understanding the interactions between contributory factors associated with accidents is important. However, the widely applied Swiss cheese model (SCM) and the human factors analysis and classification system (HFACS) oversimplify the causes of accidents and ignore the interactions among the various components of an entire system, which is the essence of systems thinking. A new systems‐based accident model that reflects the nature of the accidents and can be explained by the underlying accident model must therefore be developed. This study aims to create a new accident model that represents the interactions between the contributory factors, which can offer a systems approach to conducting a comprehensive examination of accidents. We use data obtained from the official accident reports on 94 extraordinary major coal mining catastrophes that took place from 1997 to 2011 in China. The results reveal that all latent conditions, including external factors, organizational influences, unsafe supervision, and preconditions for unsafe behaviours can influence active failures, that is, unsafe behaviours. However, only the preconditions for unsafe behaviours can influence unsafe behaviours within the SCM and HFACS frameworks. Furthermore, latent conditions can affect each other within the contributory factors interactions model. This study provides a new systems‐based accident model that allows us to examine and explain the relationships and linkages within and between complex sociotechnical systems and identify the common factors and interactions that cross human, external, and organizational factors. [ABSTRACT FROM AUTHOR]

Objective: The goals of this research are to: (i) investigate the contributing factors of major road traffic accidents using a human factors classification that follows the ideas of the human factors analysis and classification system (HFACS); (ii) quantitatively examine the relationships between the human factors of the cross levels in an entire system.Methods: This study examined 234 major road traffic accidents recorded in 27 Chinese provinces from 1997 to 2014. Odds ratio (OR) was used to quantitatively analyze the relationships among the contributing factors.Results: The frequencies of unsafe acts, violations, and inadequate regulation are the highest in five categories, 15 subcategories, and 63 indicators, respectively. This study has demonstrated a number of associations between the upper and adjacent lower levels. At the outside factors level, "failure to provide supervision for regulatory" can be viewed as a strong predictor to "formal accountability for actions," "norms and rules," and "values and beliefs." At the organizational influences level, "formal accountability for actions," "norms and rules," and "values and beliefs" were strong predictors. At the unsafe supervision level, "failure to provide oversight," "failure to initiate corrective action," and "failure to enforce rules and regulations" had strong prediction on "fatigue driving." At the preconditions for unsafe acts level, "visual limitation", "fatigue driving," and "vehicle faults" were strong predictors.Conclusions: The generic HFACS failure types were interpreted and applied successfully to the road safety context, and such examination of major accidents has provided significant findings concerning the main contributing factors of those accidents. Using the OR technique, this study has demonstrated a number of associations between the upper level and adjacent lower levels in the entire system and has found the routes to failure, which is particularly important for developing countermeasures and remediation strategies, as it ensures that these countermeasures are targeted to a wider range of systems. Furthermore, these findings demonstrate the efficiency and applicability of the HFACS as a retrospective tool for the analysis of major road traffic accidents. [ABSTRACT FROM AUTHOR]

The coal mine safety situation remains grim in China. This study proposed a hybrid systems method integrating the human factors analysis and classification system and grey relational analysis to determine the key factors causing coal mine casualties. The method was applied to analyse 94 coal mining accidents from 1997 to 2011, and the results were as follows. (a) Among the five levels, "unsafe acts" has the highest grey relational grade with accident casualties, followed by "preconditions for unsafe acts," "organizational influences," "unsafe supervision," and "external factors." (b) Among all categories, "inadequate supervision" has the closest relationship with accident casualties, followed by "violations," "environmental factors," and "errors." (c) "Naked fire in flammable and explosive places" and "untimely accident handling or improper handling measures" are important indicators affecting accident casualties. Finally, some corresponding suggestions for safe coal mine production were proposed. [ABSTRACT FROM AUTHOR]

This study aimed to provide a greater understanding of the systemic factors involved in coal mine accidents and to examine the relationships between the contributing factors across all levels of the system. Ninety-four extraordinarily major coal mine accidents that occurred in China from 1997 to 2011 were analyzed using the human factors analysis and classification system (HFACS). The empirical results showed that the frequencies of unsafe behaviors, inadequate regulation and failure to correct hidden dangers were the highest among five levels, 14 categories and 48 indicators, respectively. The odds ratio technique was applied to quantitatively examine the relationships between contributing factors. Various statistically significant associations were discovered and should receive greater attention in future attempts to develop accident measures. In addition, several strategies concerning the main contributing factors and routes to failure are proposed to prevent accidents from reoccurring in an organization. [ABSTRACT FROM AUTHOR]