A network accident causation model for monitoring railway safety

In railway systems, risk monitoring and accident causation analysis are important processes towards operational safety. This paper divides accident causal factors in a railway system into several error types, such as human and signal, and proposes a model based on a complex network for risk monitoring, where the risks of accident causal factors are quantified. This network accident causation model is used to identify accident causal factors and analyze how these factors affect each other, for example, how a signal error leads to a collision between two trains. The results of this case study show that in a complex environment, the proposed model can better identify the root causal factors by quantifying the accident causal factor risk, to find the causation chain based on the interactions among accident causal factors. Based on the analysis results, we can timely and correctly monitor the accident causal factors which have high possibility to raise faults or accidents, thereby protecting the railway system from these factors. The proposed network model provides an effective support for risk monitoring in a railway system.