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Keywords: Bayesian network, human error, conditional probabilities.

Summary

In this paper Markovian-Bayesian network models able to perform a probabilistic safety analysis of high speed or conventional railway lines and to evaluate the probability of incidents associated with the circulation of trains along the lines with special consideration of human errors are presented. Since this probability increases as trains pass through the different elements encountered along the line, all the line relevant elements, such as light and speed limit signals, rolling stock failures, falling materials, slope slides in cuttings and embankments, tunnel or viaduct entries or exits, automatic train protection systems and other elements are reproduced with a special consideration of human behavior and human error. Bayesian networks, made of a sequence of several connected Bayesian subnetworks are used. A subnetwork is associated with each element in the line that implies a concentrated risk of accident or produce a driver's attention change and also the risks associated with segments without signals where some elements add a continuous risk. All subnetworks are connected with the previous ones and some of them are multi-connected because some consequences are dependent on previous errors. Since the 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 too. The model updates the driver's attention level and accumulates the probability of accidents associated with the different elements encountered along the line. This permits the generation of 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 identification of the relevant and the irrelevant parameters and whether or not the different risk protection systems are equilibrated avoiding waste of time and money by concentrating safety improvement actions and maintenance operations only on the relevant ones. Finally, some examples are used to illustrate the model.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 110 PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 73 Probabilistic Safety Analysis of High Speed Railway Lines including Human Errors E. Castillo^1,2, Z. Grande², A. Calviño^2,3, M. Nogal⁴ and A.J. O'Connor⁴ ¹Royal Academy of Engineering, Spain ²Department of Applied Mathematics and Computational Sciences, University of Cantabria, Santander, Spain ³Complutensis University of Madrid, Spain ⁴Trinity College, Dublin, Ireland doi:10.4203/ccp.110.73 purchase the full-text of this paper Full Bibliographic Reference for this paper , "Probabilistic Safety Analysis of High Speed Railway Lines including Human Errors", in J. Pombo, (Editor), "Proceedings of the Third International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 73, 2016. doi:10.4203/ccp.110.73 Keywords: Bayesian network, human error, conditional probabilities. Summary In this paper Markovian-Bayesian network models able to perform a probabilistic safety analysis of high speed or conventional railway lines and to evaluate the probability of incidents associated with the circulation of trains along the lines with special consideration of human errors are presented. Since this probability increases as trains pass through the different elements encountered along the line, all the line relevant elements, such as light and speed limit signals, rolling stock failures, falling materials, slope slides in cuttings and embankments, tunnel or viaduct entries or exits, automatic train protection systems and other elements are reproduced with a special consideration of human behavior and human error. Bayesian networks, made of a sequence of several connected Bayesian subnetworks are used. A subnetwork is associated with each element in the line that implies a concentrated risk of accident or produce a driver's attention change and also the risks associated with segments without signals where some elements add a continuous risk. All subnetworks are connected with the previous ones and some of them are multi-connected because some consequences are dependent on previous errors. Since the 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 too. The model updates the driver's attention level and accumulates the probability of accidents associated with the different elements encountered along the line. This permits the generation of 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 identification of the relevant and the irrelevant parameters and whether or not the different risk protection systems are equilibrated avoiding waste of time and money by concentrating safety improvement actions and maintenance operations only on the relevant ones. Finally, some examples are used to illustrate the model. purchase the full-text of this paper (price £22) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £85 +P&P)
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