No abstract

The Life Quality Index (LQI) is a rational way to establish a relationship among the resources utilized to improve human safety and the expected fatalities that can be avoided by safety improvement. This article uses the LQI approach to quantify the social benefits of a number of safety management plans for a railway facility such as level crossing (LC). We apply influence diagrams (IDs), which are the extensions of Bayesian Networks, to model and assess the life safety risks. In IDs, problems of probabilistic inference, economics-based utility values, and decision alternatives are combined and optimized. The optimal decision, which maximizes total benefits to society, is obtained for the LC. As low as reasonably practicable (ALARP) case, and is a widely accepted risk acceptance criteria in the railway industry. According to the ALARP, there exists a so-called tolerable region between the regions of intolerable and negligible risks. In the tolerable region, risk is undertaken only if a benefit is desired. To quantify socioeconomic benefits, one needs to have an additional risk acceptance criterion such as LQI. In this article we apply and discuss the advantages of the LQI and the IDs for a number of safety management plans for railway LCs.

No abstract

In this paper we propose a general framework to compute limiting tolerable hazard rate (LTHR) in complex railway system. The driver machine interface (DMI) is a safety critical component of European Train Control System (ETCS). Functional failure of the DMI can affect the role of the ETCS and can lead to adverse impacts. Therefore, it is important to carry out a careful functional safety analysis of the DMI. The quantification of the LTHR of a DMI is a complex task due to a number of dependencies and uncertainties among event scenarios leading to adverse consequences. Failing to consider dependencies and uncertainties will lead to over or under estimation of the functional safety of a DMI. It motivates the investigation of using Bayesian Networks (BNs) for functional safety analysis of DMI. BNs are acyclic probabilistic graphical model and offer concise representation of dependencies and uncertainties among random variables. The BNs will be used to quantify risk reduction parameters, which will be utilized to quantify LTHR using a mathematical model.

No abstract