Bayesian Network for Managing Runway Overruns in Aviation Safety

Ayra, Eduardo S.; Insua, David Rı́os; Cano, Javier

doi:10.2514/1.i010726

Cited by 15 publications

(11 citation statements)

References 26 publications

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“…Gui et al [15] proposed a random forest-based model to predict the flight delay, and the classification accuracy of the model reached 90.2%. In [4], Eduardo et al considered the runway overrun risk in different scenarios and proposed a BN (Bayesian network) [16,17] based risk assessment model. Lukas et al [18] investigated the run-way overrun and loss of control accidents by combining physical models of aircraft dynamics with statistical dependence analysis, based on which they developed advanced FDM algorithms for safety management.…”

Section: Related Workmentioning

confidence: 99%

“…[15] proposed a random forest‐based model to predict the flight delay, and the classification accuracy of the model reached 90.2%. In [4], Eduardo et al. considered the runway overrun risk in different scenarios and proposed a BN (Bayesian network) [16, 17] based risk assessment model.…”

Section: Related Workmentioning

confidence: 99%

“…Runway overrun, which is defined as situations when aircraft extends beyond the end of runway, is a typical safety accidents concerned by pilots and airlines during this phase. Based on work [3] and a recent study [4], runway overrun accidents mainly owe to the following risk factors: long landing, unstable approach, high approach speed, visual approach, etc. Long landing, in which case an aircraft consumed too much runway length when it touched the ground, that is, the touchdown distance significantly exceeds a given threshold, can largely con‐ tribute to runway overrun accidents and it is regarded as the main risk factor among all the risk factors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A deep sequence‐to‐sequence method for accurate long landing prediction based on flight data

Kang

Shang

Feng

et al. 2021

IET Intelligent Trans Sys

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In civil aviation industry, runway overrun is a typical landing safety incident concerned by both airlines and authorities. Among various contributing factors to the runway overrun incident, long landing plays an important role. However, existing studies for long landing prediction mainly depend on classic machine learning methods and handcrafted features. As a result, they usually require much expert knowledge and provide unsatisfactory results. To address these problems, this paper proposes an innovative deep sequence-to-sequence model which utilizes QAR (Quick Access Recorder) data for accurate long landing pre-diction. Specifically, to cope with the high heterogeneity of QAR dataset, a data pre-processing procedure is first proposed which includes data cleaning, interpolation and normalization steps. Second, to avoid the noises incurred by too many QAR parameters and relieve the reliance on expert experience, the GBDT (gradient boosting decision trees) model is employed to choose the most relevant parameters as features. Then a CNN-LSTM and TG-attention encoder-decoder architecture is proposed to accurately predict future aircraft ground speed and radio height sequences, based on which the touchdown distance can be finally calculated. Experimental results on a large QAR dataset with 44,176 A321 flights validate effectiveness of the proposed method. INTRODUCTIONIn civil aviation industry, flight safety is a vital important issue concerned by both airlines and authorities. According to the Boeing statistics [1], the landing phase, which only occupies 4% of the overall flight time, contributes to 48% of the fatal accidents during 2007 to 2016. This phase also has the largestThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A deep sequence‐to‐sequence method for accurate long landing prediction based on flight data

Kang

Shang

Feng

et al. 2021

IET Intelligent Trans Sys

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“…8 While this can be useful for short horizon and operational planning, AS strategic planning at country or airline level requires focusing more on medium to long-term forecasts of occurrences. Moreover, due to the many different types of AS occurrences, there is a need for flexible models capable of forecasting occurrences of very different nature, instead of models that focus on one particular occurrence, as for example, those for runway excursions, 9 flight delays, 10 or go-around/missed approaches. 11 Thus, our aim is at providing a comprehensive modeling approach that can accurately forecast diverse AS occurrences with long or short horizons, that is, the interest lies in modeling time series of nonnegative counts, taking into account the particularities of our application domain.…”

Section: Introductionmentioning

confidence: 99%

Forecasting aviation safety occurrences

Flores

Insua

Alfaro

et al. 2022

Appl Stoch Models Bus & Ind

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We present a general framework for aviation safety occurrence forecasting. This is a major component of a methodology for aviation safety risk management at national level. It covers novel models as well as novel combinations of earlier models. Having good quality occurrence and severity forecasting models is paramount to properly manage risks, maintain the confidence of its users and preserve the status of aviation as a safe transportation mode. The problem is involved due to the presence of complex effects like seasonality, trends, or stress that impact the rates of various occurrences and the uncertainty about future number of operations.

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“…One way to improve understanding is by modeling accidents. Several researchers have used Bayesian networks to identify causal factors and assess risk (Ancel & Shih, 2012;Ancel et al, 2015;Ayra et al, 2019;Xiao et al, 2020;Uğurlu et al, 2020). Ancel et al (2015) developed an object-oriented Bayesian network (OOBN), based on HFACS, to model Part 121 and 135 LOC-I accidents.…”

Section: Introductionmentioning

confidence: 99%

Analysis of General Aviation Fixed-Wing Aircraft Accidents Involving Inflight Loss of Control Using a State-Based Approach

2021

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Inflight loss of control (LOC-I) is a significant cause of General Aviation (GA) fixed-wing aircraft accidents. The United States National Transportation Safety Board’s database provides a rich source of accident data, but conventional analyses of the database yield limited insights to LOC-I. We investigate the causes of 5,726 LOC-I fixed‑wing GA aircraft accidents in the United States in 1999–2008 and 2009–2017 using a state-based modeling approach. The multi-year analysis helps discern changes in causation trends over the last two decades. Our analysis highlights LOC-I causes such as pilot actions and mechanical issues that were not discernible in previous research efforts. The logic rules in the state-based approach help infer missing information from the National Transportation Safety Board (NTSB) accident reports. We inferred that 4.84% (1999–2008) and 7.46% (2009–2017) of LOC-I accidents involved a preflight hazardous aircraft condition. We also inferred that 20.11% (1999–2008) and 19.59% (2009–2017) of LOC-I accidents happened because the aircraft hit an object or terrain. By removing redundant coding and identifying when codes are missing, the state-based approach potentially provides a more consistent way of coding accidents compared to the current coding system.

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Bayesian Network for Managing Runway Overruns in Aviation Safety

Cited by 15 publications

References 26 publications

A deep sequence‐to‐sequence method for accurate long landing prediction based on flight data

A deep sequence‐to‐sequence method for accurate long landing prediction based on flight data

Forecasting aviation safety occurrences

Analysis of General Aviation Fixed-Wing Aircraft Accidents Involving Inflight Loss of Control Using a State-Based Approach

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