Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors

Valdés, Rosa María Arnaldo; Cheng, Schon Z.Y. Liang; Comendador, Fernando Gómez; Nieto, Francisco Javier Sáez

doi:10.3390/e20120969

Cited by 13 publications

(13 citation statements)

References 22 publications

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“…For this reason, we will calibrate the proposed model using technical data when possible, and illustrative data from experts' judgment otherwise, see the Appendix. The output data will be assumed to be representative of the case study, although it should be validated in a future stage using some of the approaches proposed in the literature (e.g., see [19,20]).…”

Section: Impact Modelmentioning

confidence: 99%

Trajectory-Based, Probabilistic Risk Model for UAS Operations

Usach¹,

Vila²,

Gallego³

2020

Risk Assessment in Air Traffic Management

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To enable the safe integration of Unmanned Aircraft System (UAS) into the civil airspace, the European Aviation Safety Agency (EASA) has elaborated a new regulatory framework that is operation-centric and risk-based. Based on this principle, gaining authorization to conduct certain types of operations depends on a safety risk assessment. To harmonize this process, the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) released a qualitative methodology called Specific Operation Risk Assessment (SORA). However, SORA is not a complete safety assessment tool since, in some cases, a quantitative risk analysis is still required. This work develops a probabilistic risk model that extends SORA to evaluate the ground risk and the air risk components along a specified UAS trajectory quantitatively. The proposed model is supplied with illustrative data and is validated in a representative UAS mission. In the future, the risk model will be exploited to develop a decision tool for determining the minimum-risk trajectory when multiple, alternative routes are available.

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Section: Impact Modelmentioning

confidence: 99%

Trajectory-Based, Probabilistic Risk Model for UAS Operations

Usach¹,

Vila²,

Gallego³

2020

Risk Assessment in Air Traffic Management

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“…The aim of the networks was to help to identify the relevant variables in the process, and to understand the causal relationships and interdependences between factors influencing the complexity and the uncertainties associated to those factors [19][20][21]. The outcome of these models was two-fold.…”

Section: Introductionmentioning

confidence: 99%

“…They are particularly useful to capture and analyse causality and influence's relationships, and they are a convenient and coherent way to represent uncertainty in uncertainty models. In particular, they have the capacity to model propagation of multi-directional uncertainty forward and backward; thus, they are a useful tool for both predicting the performance of a system or diagnosing the causes of a certain system outcome [20,21].…”

mentioning

confidence: 99%

Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions

Comendador

Valdés

Díaz

et al. 2019

Entropy

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Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories’ uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the “complexity generators” in the “complexity metrics”. Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve.

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“…From this perspective, statistical models that can establish the qualitative relationship between different levels of the pyramid will be advantageous in comprehending the proximity to fatalities [ 9 ]. In our previous work [ 13 ], we followed a series of steps in extracting serious incident data for Bayesian Network (BN) construction as well as searching possible scenarios where influential causes contributed to this category of accidents. In our research [ 14 ], we have completed the analysis adding major incidents and updated the BN model providing relations between serious (near accidents) and major incidents, which have been established through the connections between factors and events in different categories of the incident.…”

Section: Introductionmentioning

confidence: 99%

Detection of Common Causes between Air Traffic Serious and Major Incidents in Applying the Convolution Operator to Heinrich Pyramid Theory

Cheng

Valdés

Comendador

et al. 2019

Entropy

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Heinrich’s pyramid theory is one of the most influential theories in accident and incident prevention, especially for industries with high safety requirements. Originally, this theory established a quantitative correlation between major injury accidents, minor injury accidents and no-injury accidents. Nowadays, researchers from different fields of engineering also apply this theory in establishing quantitatively the correlation between accidents and incidents. In this work, on the one hand, we have detected the applicability of this theory by studying incident reports of different severities occurred in air traffic management. On the other hand, we have deepened the analysis of this theory from a qualitative perspective. For this purpose, we have applied the convolution operator in identifying correlations between contributing causes to different incident severities, also known as precursors to accidents, and system failures. The results suggested that system failures are mechanisms by which the causes are manifested. In particular, the same underlying cause can be manifested through different failures which contribute to incidents with different severities. Finally, deriving from this result, an artificial neuronal network model is proposed to recognize future causes and their possible associated incident severities.

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Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors

Cited by 13 publications

References 22 publications

Trajectory-Based, Probabilistic Risk Model for UAS Operations

Trajectory-Based, Probabilistic Risk Model for UAS Operations

Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions

Detection of Common Causes between Air Traffic Serious and Major Incidents in Applying the Convolution Operator to Heinrich Pyramid Theory

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