A Markovian–Bayesian Network for Risk Analysis of High Speed and Conventional Railway Lines Integrating Human Errors

The article provides a new Markovian-Bayesian network model to evaluate the probability of accident associated with the circulation of trains along a given high speed or conventional railway line with special consideration to human error. This probability increases as trains pass throughout the different elements encountered along the line. A Bayesian network, made up of a sequence of several connected Bayesian subnetworks, is used. A subnetwork is associated with each element in the line that implies a concentrated risk of accident or produces a change in the driver's attention, such as signals, tunnel, or viaduct entries or exits, etc. Bayesian subnetworks are also used to reproduce segments without signals where some elements add continuous risks, such as rolling stock failures, falling materials, slope slides in cuttings and embankments, etc. All subnetworks are connected with the previous one and some of them are multi-connected because some consequences are dependent on previous errors. Because driver's attention plays a crucial role, its degradation with driving time and the changes due to seeing light signals or receiving acoustic signals is taken into consideration. The model updates the driver's attention level and accumulates the probability of accident associated with the different elements encountered along the line. This permits us to generate a continuously increasing risk graph that includes continuous and sudden changes indicating where the main risks appear and whether or not an action must be taken by the infrastructure manager. Sensitivity analysis allows the relevant and irrelevant parameters to be identified avoiding wastes of time and money by concentrating safety improvement actions only on the relevant ones. Finally, some examples are used to illustrate the model. In particular, the case of the Orense-Santiago de Compostela line, where a terrible accident took place in 2013.

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