Investment into Human Risks in Railways and Decision Optimization

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.

Life Quality Index–based cost–benefit analysis of equipping life preservers aboard airplanes

“…The LQI is a rational tool to establish a relationship between the resources utilized to improve human safety and reduce the expected fatalities. 9 The tool, which was demonstrated to be highly effective, 10 was specified by ISO 2394:2015 (E) 11 as a key optimization approach regarding the life safety. The LQI can be expressed as 11…”

SIL determination as a utility-based decision process

“…Some have used the concept to draw general models for optimum investment on risk reduction measures (Abrahamsen and Asche, 2011;Aven and Hiriart, 2011;Li et al, 2009;Abrahamsen and Aven, 2012) and others have used the theory in practical applications (Pourali and Mosleh, 2012;Mahboob, 2014;Dong and Frangopol, 2015;Zhang et al, 2013;Mahboob et al, 2015;Špačková and Straub, 2015). These papers are mainly focused on optimising the cost of overall risk reduction measures, and none of them have addressed the SIL determination process in particular.…”

Matrix-vector algorithms of local posteriori inference in algebraic bayesian networks on quanta propositions

“…Следует отметить, что рассматриваются именно пропозициональные формулы, а не их истинностные значения. Более того, в дальнейших выкладках рассматриваются классы эквивалент-ности пропозициональных формул; в частности, конъюнкции над одними и теми же формулами, но с разным порядком следования последних, не различаются [2,3,8,12]. , 22].…”

“…Байесовские сети, в том числе алгебраические байесовские сети (АБС) [2,3] и связанные с ними конструкции, являются одним из видов вероятностных графических моделей. Байесовские сети все шире используются в системах распознавания образов [4,5], системах моделирования поведения [6], системах моделирования рисков и обнаружения отказов [7][8][9], оценивания повреждений и мониторинга состояния здоровья [10][11][12][13], а также при анализе климата и окружающей среды [14,15].…”