Further development of a Causal model for Air Transport Safety (CATS): Building the mathematical heart

Ale, B.J.M.; Bellamy, Linda J.; Boom, R. van der; Cooper, Joseph L.; Cooke, Roger M.; Goossens, Louis; Hale, Andrew; Kurowicka, Dorota; Morales, O.; Roelen, Alfred; Spouge, J R

doi:10.1016/j.ress.2009.02.024

Cited by 77 publications

(34 citation statements)

References 10 publications

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“…Bayesian Belief Networks (BBNs) are often used to develop models which quantify the probabilities of accident outcomes [24][25][26][27]. A recently developed 'Causal Model for Air Transport Safety' (CATS) integrates BBNs with event sequence diagrams and fault trees in a complex linear model [25].…”

Section: Network Modelling Approachesmentioning

confidence: 99%

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Aviation Human Error Modelled as a Production Process

Pons¹,

Dey

2015

TOERGJ

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As technology systems have become more complex, so it is increasingly difficult for human operators to comprehend how the system is behaving. There is a need to better understand the causes of human-error in the context of the situational variables. The specific case under examination is the process of landing an aircraft. This paper applies a production engineering perspective. By production-process, we refer to the set of actions that are necessary to move a system from one state to a desired new state. Such processes involve mechanisms for taking inputs and converting them to the desired outputs, under certain controls and constraints. Human error is thus treated as a constraint on the processes. A novel process methodology is developed to represent how technical systems, situational variables, external factors, and human error evolve over time in a process. The applicability is demonstrated with a dataset of case studies. In parallel, a new categorization of human errors is derived for this situation. The method permits cases to be examined individually, and also collectively for patterns. The results show that the initial landing approach is often the point of error initiation. For the specific case of landing accidents the methodology identifies that the failure causality was the lack of attention to establish procedure at the very start of descent, followed by persistent disregard of disconfirmatory evidence at the initial landing approach, especially under conditions of poor visibility. The paper makes a methodological contribution as well as suggesting new insights for how human error occurs at landing.

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Section: Network Modelling Approachesmentioning

confidence: 99%

“…A recently developed 'Causal Model for Air Transport Safety' (CATS) integrates BBNs with event sequence diagrams and fault trees in a complex linear model [25]. This model is particularly complex and has a reliance on the values of the probability assigned to each outcome.…”

Section: Network Modelling Approachesmentioning

confidence: 99%

Aviation Human Error Modelled as a Production Process

Pons¹,

Dey

2015

TOERGJ

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“…Along the same lines the Causal Model for Air Transport Safety was developed and quantified on the basis of an analysis of some 10000 accidents and incidents in the ADREP data base of air traffic incidents and accidents (Ale et al 2009). This model later was used among others to search for the most likely cause of an airline crash in the Netherlands in 2009 (Ale et al, 2010).…”

Section: Evaluating Accidentsmentioning

confidence: 99%

Risk analysis and big data

Ale

2016

Safety and Reliability

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Big Data can help overcome various problems that exist in present risk analysis practices. By analysing systems as a whole, it is no longer necessary to define in advance what a failure is and what a success is. It is also possible to evaluate how factors that are considered to promote success can combine into catastrophic failures. Big in Big Data is relative. What was called big data 25 years ago is now called small. The continuous development of analysis techniques over the years have resulted in several operational models that use the concept of Big Data. They will become better as the technology and the accessibility of data further improves. With this new generation of systems models, accidents and incidents do not have to wait for analysis to after the fact. They can be studied beforehand in a model. Replacing hindsight by foresight can help to make the world safer, if we desire to do so.

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“…Taking the variability of the into account earlier only was possible by sheer endless repetition of the calculations. This resulted in the quantification of occupational risks (WORM) of air traffic risk (CATS) and currently the quantification of risk in chemical plants including the uncertainty in the estimates (Ale et al, 2006a(Ale et al, , 2008(Ale et al, , 2009). The realisation that normal accidents (Perrow, 1984) were the materialisation of a combination of extreme, rare but possible values of variables, including the variability of human behaviour led to the attempt to use distributed initial frequencies rather than point estimates.…”

Section: Uncertaintiesmentioning

confidence: 99%

Deep and shallow uncertainty in messaging climate change

Ale¹

2013

Safety, Reliability and Risk Analysis

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Forty years of development in the science and technology of estimating and quantifying risk, of understanding of human behaviour and human rationale has made decision makers more informed. We are in a much better position now, than forty years ago in estimating probabilities, consequences, and damages, and in estimating and dealing with the associated uncertainties in an organised way. This did not take away the fundamental questions about the acceptability of activities bearing risk. It did not take away the differences between people and groups of people and it did not take away that these decisions are difficult.mind. More worrying is that acquired knowledge is disappearing. Knowledge that is not obsolete and irrelevant, but still is necessary to keep our systems going and risk under control.There has been a time without internet and without tablets or personal computers. A time when information travelled slowly, accidents became known only to a limited number of people and often long after the event. Where conferences such as these were the medium to exchange this information and papers were typed on typewriters. Forty years ago, a number of nasty acci-

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Further development of a Causal model for Air Transport Safety (CATS): Building the mathematical heart

Cited by 77 publications

References 10 publications

Aviation Human Error Modelled as a Production Process

Aviation Human Error Modelled as a Production Process

Risk analysis and big data

Deep and shallow uncertainty in messaging climate change

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