Developing Safety Indicators for New Zealand Airspace: Analysis of Loss-of-Separation Incidents

Majumdar, Arnab; Dupuy, Marie-Dominique; Ochieng, Washington Yotto; Nalder, Peter

doi:10.3141/1951-11

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Particularly, in the aviation sector, Walker [ 10 ] has established a risk pyramid with quantitative relation between occurrences, incidents, and accidents based on data registered in black boxes with the purpose of improving flight data monitoring system. Majumdar et al [ 11 ] have applied this theory directly to develop safety indicators using the data of loss of separation (LOS) incidents registered in airspaces of New Zealand and the United Kingdom; however, unlike the quantitative relation considered in Heinrich’s pyramid, Nazeri and Lance [ 12 ] have applied this theory in looking for a qualitative relation between accidents and incidents through their underlying factors.…”

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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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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“…Majumdar [6] applied log-linear modeling technique to analyze seven factors involved in loss-of-separation incidents. Hansen and Zhang [7] tested the hypothesis that adverse operating conditions lead to higher incident rates in air traffic control.…”

Section: Introductionmentioning

confidence: 99%

Contrast-Set Mining of Aircraft Accidents and Incidents

Nazeri

Barbará

Jong

et al. 2008

Lecture Notes in Computer Science

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Abstract. Identifying patterns of factors associated with aircraft accidents is of high interest to the aviation safety community. However, accident data is not large enough to allow a significant discovery of repeating patterns of the factors. We applied the STUCCO 1 algorithm to analyze aircraft accident data in contrast to the aircraft incident data in major aviation safety databases and identified factors that are significantly associated with the accidents. The data pertains to accidents and incidents involving commercial flights within the United States. The NTSB accident database was analyzed against four incident databases and the results were compared. We ranked the findings by the Factor Support Ratio, a measure introduced in this work.

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Air Traffic Control Complexity and Safety

Majumdar

Ochieng

2007

Transportation Research Record: Journal of the Transportation R

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“Air traffic control (ATC) complexity” can be defined as the interaction between air traffic and the sector characteristics through which this air traffic flies. Research indicates that complexity is the prime determinant of the air traffic controller's workload. Research has attempted to define and classify complexity variables that have been used to assess the capacity of en route airspace in Europe and North America. Such variables can also be used to assist in ATC sector design by highlighting those complexity factors that can lead to high workload and potentially risky scenarios for the controller. With the use of a structured interview technique, a study analyzed these ATC complexity variables for their impact on controller workload and safety. Seventy-nine air traffic controllers were interviewed about the factors that affected their workload. These controllers were based in 14 area control centers in Europe, including those of high complexity, such as Maastricht, Netherlands, as well as in nine centers in Asia, one in Africa, and Mumbai, India, a region with major traffic growth in recent years. On the basis of these interviews, a taxonomy of more than 50 complexity variables subdivided into 11 major groupings, such as traffic mix and entry and exit points, has been developed. In addition to rating of the impact of the individual complexity factors to assess their effect on airspace, interactions between complexity variables were deduced from the interviews. These interactions enable assessment of the factors that add to layers of complexity for controllers. Such a taxonomy can assist airspace planners in designing sectors that avoid complex ATC situations. How the impact of such complexity factors can be investigated in sector design is outlined.

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Developing Safety Indicators for New Zealand Airspace: Analysis of Loss-of-Separation Incidents

Cited by 4 publications

References 5 publications

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

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

Contrast-Set Mining of Aircraft Accidents and Incidents

Air Traffic Control Complexity and Safety

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