A Bayesian-Network-based Approach to Risk Analysis in Runway Excursions

Calle-Alonso, Fernando; Pérez, Carlos J.; Ayra, Eduardo S.

doi:10.1017/s0373463319000109

Cited by 23 publications

(6 citation statements)

References 24 publications

(24 reference statements)

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“…Based on the analysis of aircraft ground points and remaining runway lengths by using QAR data, Roy constructed risk maps of aircraft overshooting runways under different conditions and analyzed the risk degree of aircraft overshooting runways according to the risk maps [7], [8]. In the ACRP series of research reports [9] released by FAA, the probability range of hedge/off-runway risk, namely, each safety objective level, was specified, and the influencing factors were analyzed to evaluate the hedge/off-runway risk.…”

Section: Introductionmentioning

confidence: 99%

Study on probability evaluation method of aircraft overshoot runway based on logistic regression

Liu

Chong²,

Bai³

et al. 2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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A runway safety zone is an area at both ends and sides of a runway to protect the safety of an aircraft when it overruns/veers off the runway. A civil airport runway safety zone is a rectangular area composed of a runway end safety zone and lifts belt. The military airport has no clear concept of a runway safety zone, but there are soil runways, flat areas, terminal safety lanes, and other facilities at both ends and sides of the runway, which play the same role as the safety zone. The research method of aircraft overshoot runway risk is selected. Based on analyzing the characteristics of the influencing factors of aircraft overshooting runways, according to the dichotomous characteristics of the influencing factors and accident results, a logistic regression analysis based on the probability assessment method of aircraft overshooting/overshooting runway is proposed.

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Section: Introductionmentioning

confidence: 99%

Study on probability evaluation method of aircraft overshoot runway based on logistic regression

Liu

Chong²,

Bai³

et al. 2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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“…Bayesian networks (BNs), a classical probabilistic graphical model, combine probability theory and graph theory to deal with uncertain problems, and are successfully applied to a wide range of domains such as prediction [5,34], risk analysis [4,14]. There are two components in this field: the BN structure and the BN parameter.…”

Section: Introductionmentioning

confidence: 99%

A decomposition structure learning algorithm in Bayesian network based on a two-stage combination method

Guo

2022

Complex Intell. Syst.

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Decomposition hybrid algorithms with the recursive framework which recursively decompose the structural task into structural subtasks to reduce computational complexity are employed to learn Bayesian network (BN) structure. Merging rules are commonly adopted as the combination method in the combination step. The direction determination rule of merging rules has problems in using the idea of keeping v-structures unchanged before and after combination to determine directions of edges in the whole structure. It breaks down in one case due to appearances of wrong v-structures, and is hard to operate in practice. Therefore, we adopt a novel approach for direction determination and propose a two-stage combination method. In the first-stage combination method, we determine nodes, links of edges by merging rules and adopt the idea of permutation and combination to determine directions of contradictory edges. In the second-stage combination method, we restrict edges between nodes that do not satisfy the decomposition property and their parent nodes by determining the target domain according to the decomposition property. Simulation experiments on four networks show that the proposed algorithm can obtain BN structure with higher accuracy compared with other algorithms. Finally, the proposed algorithm is applied to the thickening process of gold hydrometallurgy to solve the practical problem.

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“…Based on these data, the flight process of aircraft can be analysed to assist airline decision making and provide flight safety warnings. With the recent advance in big data methodologies and applications, many researchers began to investigate flight safety with QAR dataset, such as detecting abnormal flights [5, 6], investigating risk models [7, 8], predicting safety incidents [9, 10] etc. However, existing works are mainly based on traditional machine learning methods or classic statistical models, such as the ANOVA analysis and multiple regression method [11], the SVM‐based landing risk model [2] etc.…”

Section: Introductionmentioning

confidence: 99%

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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A Bayesian-Network-based Approach to Risk Analysis in Runway Excursions

Cited by 23 publications

References 24 publications

Study on probability evaluation method of aircraft overshoot runway based on logistic regression

Study on probability evaluation method of aircraft overshoot runway based on logistic regression

A decomposition structure learning algorithm in Bayesian network based on a two-stage combination method

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

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