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Wang, F.; Yuan, J.; Liu, X.; Wang, P.; Xu, M.; Li, X.; Li, H. Flight Operations on the Risk of Runway Excursions. Encyclopedia. Available online: https://encyclopedia.pub/entry/54760 (accessed on 10 May 2024).
Wang F, Yuan J, Liu X, Wang P, Xu M, Li X, et al. Flight Operations on the Risk of Runway Excursions. Encyclopedia. Available at: https://encyclopedia.pub/entry/54760. Accessed May 10, 2024.
Wang, Feiyin, Jintong Yuan, Xiaochen Liu, Pengtao Wang, Mao Xu, Xiaoyu Li, Hang Li. "Flight Operations on the Risk of Runway Excursions" Encyclopedia, https://encyclopedia.pub/entry/54760 (accessed May 10, 2024).
Wang, F., Yuan, J., Liu, X., Wang, P., Xu, M., Li, X., & Li, H. (2024, February 05). Flight Operations on the Risk of Runway Excursions. In Encyclopedia. https://encyclopedia.pub/entry/54760
Wang, Feiyin, et al. "Flight Operations on the Risk of Runway Excursions." Encyclopedia. Web. 05 February, 2024.
Flight Operations on the Risk of Runway Excursions
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This entry is adapted from the peer-reviewed paper 10.3390/app14030975

The Quick Access Recorder (QAR), as an onboard device used for monitoring and recording flight parameters, has been extensively installed on various types of aircraft. Ensuring safety during the landing phase has always been a top priority for the civil aviation industry.

QAR data runway excursion K-means ++ operating characteristics flight safety

1. Introduction

Ensuring safety during the landing phase has always been a top priority for the civil aviation industry. The International Air Transport Association (IATA) Safety Report for 2021 reveals that nearly 80% of all aviation accidents occur during the landing phase. Among the latent conditions that contribute to runway excursions, flight operations account for 28% of the impact, while the influence of the flight operations training system is estimated at 16%. These findings highlight the critical nature of the landing phase in ensuring aviation safety, with operational procedures and training programs playing crucial roles in safeguarding against accidents.
Runway excursions have received significant attention in the civil aviation industry. The IATA’s 2021 Safety Report [1] identified runway excursions as a high-risk category, constituting 24% of all accidents prior to the COVID-19 pandemic. Over the past decade (2012–2021), runway excursions have been the most common accident category, with 138 occurrences. This category ranks as the third leading cause of fatal accidents, resulting in 93 fatalities across nine accidents. These accidents occurred during aircraft takeoff or landing and were influenced by factors such as unstable approaches, high landing speeds, and slippery runways. According to the IATA Annual Safety Report—2022, there was one fatality attributed to runway excursions last year, with six occurrences of non-fatal incidents involving runway excursions [2]. According to A Statistical Analysis of Commercial Aviation Accidents by Airbus, the analysis of data from 2002 to 2022 highlights that runway excursions rank as the third leading cause of fatal accidents and represent the primary factor in aircraft structural damage. Runway excursions account for 17% of fatal accidents within various accident categories. Furthermore, within the distribution of accidents based on aircraft damage, runway excursions constitute 36% of cases [3].
Research on runway excursions can generally be categorized into two main types: accident report analysis and exploration of causal and influencing factors. Studies that focus on identifying causes and analyzing factors typically rely on accident investigation reports to determine the contributing factors and propose corresponding improvement measures. Another approach involves analyzing past accidents using accident analysis models to classify and analyze the risks associated with runway excursions, thus providing risk management strategies. Alternatively, researchers may establish indicator systems and weights based on event analysis and expert opinions to conduct risk analysis and prediction for runway excursions [4][5][6].
Another category of research focuses on the analysis of exceedance events and the study of distances and landing trajectories in runway excursions based on Quick Access Recorder (QAR) data [7][8][9]. In the context of runway excursion research, there has been limited utilization of QAR data for operational risk analysis. Current studies based on QAR data predominantly concentrate on the characteristics and operational risks of operations, but these studies are currently less focused on specific operational nodes. Research on operational characteristics emphasizes feature identification with limited exploration of accident risks. Studies on operational risks mostly focus on analyzing the entire flight process or specific parameters, with limited research combining and analyzing flight parameters.

2. Runway Excursion

In summary, research on runway excursions covers multiple aspects and can generally be divided into two categories: studies based on accident report data and those based on flight data. This section provides a brief introduction to research conducted in these two areas. Okafor et al. [4] conducted a statistical analysis of summaries, accident reports, and perspectives from stakeholders to identify the influencing factors of runway excursions. They discovered that accidents occur when there is a combination of human and engineering system failures. Analyzing accident reports has always been crucial for ensuring safety and preventing such incidents. These reports are typically presented in textual form, requiring the extraction and analysis of key information. Chang et al. [5] employed the SHELLO model to classify human-induced risk factors and formulated four prioritized risk management strategies for airline pilots. These strategies aim to reduce the occurrence of runway excursions. Zhang et al. [6] established a predictive model for runway excursions, enabling the anticipation of such incidents. They validated their model using data from aviation companies. Wang Lei, Ren Young, et al. [7] explored the impact of crucial flare operations conducted by pilots on long-distance landings and hard landing events using QAR data. Through variance analysis, they identified flight parameter characteristics during the initiation of flaring and throughout the entire flaring process that are associated with abnormal landing events. They found that flare operations have a significant impact on landing distance and vertical landing acceleration. Li et al. [8] proposed a landing trajectory correction method that integrates ground speed and runway position information. They established a dynamic off-runway risk assessment model based on QAR data, uncovering key factors related to off-runway risk during the landing phase. In the study of exceedance issues, Lv et al. [9] introduced a novel approach that utilizes QAR data on a large scale. They categorized flight cases using exceedance danger lines and ultimately employed three machine learning models to examine the relationship between exceedance risk indices and flight parameters. Distefano et al. [10] used multiple correspondence analysis to determine the correspondence between various aspects of offset runway excursions, identifying variable combinations worthy of further study and providing a method for runway risk assessment for civil aviation organizations. Wang et al. [11] constructed a safety control structure diagram for offset runway excursions, identifying unsafe control behaviors and analyzing their key causes. Odisho et al. [12] applied machine learning to establish a predictive model for perceiving unstable approach risks, thereby reducing the occurrence of offset runway excursions by predicting the risk of unstable approaches. Olive et al. [13] utilized S-mode data to analyze the contributing factors of offset runway excursions, proposing a risk assessment model for such occurrences. Mascio et al. [14] calculated risk values for over 1300 points around runways using preliminary data provided by airport management agencies, evaluating the current safety level and providing a risk map to identify the areas with the highest risk of offset runway excursions. Distefano and others [15] employed association rule methods and the Apriori algorithm to determine that the most important variables for all types of offset runway excursions are aircraft type, with events of severity classified as major and hazardous being associated with small aircraft, while events classified as catastrophic are associated with medium to large aircraft. Ketabdari et al. [16] used simulators to model the impact of various meteorological variables on aircraft operations, indicating that gusts, wind speed, and particularly crosswinds are the primary weather factors influencing offset runway excursions. Vorobyeva et al. [17] integrated data from various sources to enhance understanding of runway surface conditions in order to reduce the risk of offset runway excursions, strengthening flight safety and environmental protection. Mauro et al. [18] analyzed the functional complexity faults leading to offset runway excursions and discussed detection and handling prior to events. Their research indicates that enhancing system safety technology may only increase system complexity, inevitably leading to more frequent faults. Therefore, in-depth analysis and management of subsystems are crucial for addressing functional complexity faults. Wang and Holzafel [19] established a model to investigate offset runway excursions and abnormal runway contact, analyzing landing gear stress and validating the model using QAR data.
The existing research has the following limitations: Regarding studies based on historical accident data, they can only analyze the trends of accidents and their influencing factors, while the available information from accident data is limited. Studies based on QAR data usually have a broader focus on stages of research without considering the varying impacts of operations at each specific stage of flight.

3. Other Flight Safety Studies

In addition to research on mitigating runway excursions, other studies on aviation safety primarily focus on flight risks, predictive warnings, and feature extraction. In the realm of a quantitative assessment of hard landing risk, Wang Lei et al. [20] applied statistical modeling to establish a quantitative assessment model for heavy landing risk, evaluating the likelihood and severity of occurrences based on parameter distribution functions and algorithms and calculating risk levels. They constructed a landing overrun analysis model based on QAR data, utilizing Bayesian network [21] analysis and employing GTT algorithms for parameter learning to establish a landing overrun risk Bayesian network model. Wang Le et al. [22] divided 128 cases of QAR data into two groups based on the identification thresholds for normal landings and long landings, conducted inter-group comparisons of flight parameters, established logistic regression and linear regression models, and analyzed the potential flight operations that lead to performance differences in long landing events, ultimately identifying key operations that affect landing distance during flare. Wang et al. [23] also conducted a study on the correlation between age and flight safety performance, revealing a direct impact of age on the exceedance rate of pilots aged 41–45 and 56–60. Zhang et al. [24] utilized the Apriori algorithm to mine association rules within exceedance events and their correlation with environmental conditions, finding that speed exceedances during the approach process at 500–50 feet are more likely to lead to speed deviations at altitudes below 50 feet during the approach process. Li et al. [25] developed an interpretable model called IMTCN, which can accurately pinpoint key flight parameters and the corresponding moments with the greatest impact on safety incidents. Chen et al. [26] proposed a risk assessment method for deep learning using LSTM-DNN with variable fuzzy recognition of entropy weight, targeting high-altitude approach and landing risks.
In the field of trend prediction and warning, Westphal et al. [27] developed an aircraft cockpit risk sustained warning system based on fuzzy logic. Huang et al. [28][29] proposed dynamic linked list QAR decoding technology, intelligently diagnosing and predicting aircraft system faults. Zhao et al. [30] established a geometric determination method and introduced a hidden Markov model backpropagation neural network to infer and predict the status of aircraft flight. Utilizing ACARS, Lu et al. [31] established a high-precision trajectory data prediction model. Fu et al. [32] utilized long short-term memory networks to establish a flight trajectory prediction model for time series flight data. Qian et al. [33] proposed a flight trajectory prediction method based on composite gated recurrent units, achieving flight trajectory prediction. Through simulation analysis, it was discovered that compared with gated recurrent units and long short-term memory network models, it has the smallest error.
In the field of feature extraction and parameter estimation, Wang et al. [34] integrated the extended Kalman filter and improved Bryson–Frazier smoother algorithm based on QAR data. This, in turn, enhanced the precision of metric estimation. On the other hand, Korsun et al. [35] used satellite navigation systems and wind speed identification methods to estimate the measurement errors of the angle of attack and sideslip angle during flight tests. Lastly, Sun et al. [36] proposed the use of a differential testing analysis method for extracting pertinent information from exceedance events.

References

  1. IATA. IATA Safety Report 2021; IATA: Montreal, QC, Canada, 2022.
  2. IATA. IATA Annual Safety Report-2022; IATA: Montreal, QC, Canada, 2023.
  3. Airbus, S.A.S. A Statistical Analysis of Commercial Aviation Accidents 1958–2022. 2023. Available online: https://accidentstats.airbus.com (accessed on 19 November 2023).
  4. Okafor, E.G.; Jemitola, P.O.; Soladoye, M.A. Assessment of runway excursion causal factors and mitigation strategies. Niger. J. Technol. 2018, 37, 619–625.
  5. Chang, Y.; Yang, H.; Hsiao, Y. Human risk factors associated with pilots in runway excursions. Accid. Anal. Prev. 2016, 94, 227–237.
  6. Zhang, Y.; Sun, Y.; Chen, Y. Aircraft runway excursion prediction model based on exponential weight. In Proceedings of the 2016 11th International Conference on Reliability, Maintainability and Safety (ICRMS), Hangzhou, China, 26–28 October 2016; pp. 1–5.
  7. Wang, L.; Ren, Y.; Wu, C. Effects of flare operation on landing safety: A study based on ANOVA of real flight data. Saf. Sci. 2018, 102, 14–25.
  8. Li, X.; Zhang, L.; Shang, J.; Li, X.; Qian, Y.; Zheng, L. A Runway Overrun Risk Assessment Model for Civil Aircraft Based on Quick Access Recorder Data. Appl. Sci. 2023, 13, 9828.
  9. Lv, H.; Yu, J.; Zhu, T. A novel method of overrun risk measurement and assessment using large scale QAR data. In Proceedings of the 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService), Bamberg, Germany, 26–29 March 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 213–220.
  10. Distefano, N.; Leonardi, S. Aircraft runway excursion features: A multiple correspondence analysis. Aircr. Eng. Aerosp. Tec. 2019, 91, 197–203.
  11. Wang, J.; Zhang, Y.; Zhang, C. Unsafe control action of runway excursion based on STPA. J. Civ. Aviat. Univ. China 2019, 37, 46–50.
  12. Odisho, E.; Truong, D.; Joslin, R.E. Applying Machine Learning to Enhance Runway Safety Through Runway Excursion Risk Mitigation. In Proceedings of the 2021 Integrated Communications Navigation and Surveillance Conference (ICNS), Dulles, VA, USA, 19–23 April 2021; pp. 1–10.
  13. Olive, X.; Bieber, P. Quantitative Assessments of Runway Excursion Precursors using Mode S data. arXiv 2019, arXiv:1903.11964.
  14. Di Mascio, P.; Cosciotti, M.; Fusco, R.; Moretti, L. Runway Veer-Off Risk Analysis: An International Airport Case Study. Sustainability 2020, 12, 9360.
  15. Distefano, N.; Leonardi, S. Apriori Algorithm for Association Rules Mining in Aircraft Runway Excursions. Civ. Eng. Archit. 2020, 8, 206–217.
  16. Ketabdari, M.; Toraldo, E.; Crispino, M. Numerical Risk Analyses of the Impact of Meteorological Conditions on Probability of Airport Runway Excursion Accidents. In Computational Science and Its Applications–ICCSA 2020, Proceedings of the 20th International Conference, Cagliari, Italy, 1–4 July 2020; Springer: New York, NY, USA, 2020; Volume 12249, pp. 177–190.
  17. Vorobyeva, O.; Bartok, J.; Šišan, P.; Nechaj, P.; Gera, M.; Kelemen, M.; Polishchuk, V.; Gaál, L. Assessing the Contribution of Data Mining Methods to Avoid Aircraft Run-Off from the Runway to Increase the Safety and Reduce the Negative Environmental Impacts. Int. J. Environ. Res. Public Health 2020, 17, 796.
  18. Mauro, R.; Sherry, L. Confronting Functional Complexity Failures: The Case of a Runway Excursion in Munich. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Washington, DC, USA, 28 October–1 November 2019; Volume 63, pp. 116–120.
  19. Wang, C.; Holzapfel, F. Modeling of the aircraft landing behavior for runway excursion and abnormal runway contact analysis. In Proceedings of the 2018 AIAA Modeling and Simulation Technologies Conference, Atlanta, Georgia, 25–27 June 2018; p. 1166.
  20. Wang, L.; Sun, R.; Wu, C.; Cui, Z.; Lu, Z. A flight QAR data based model for hard landing risk quantitative evaluation. China Saf. Sci. J. 2014, 24, 88–92.
  21. Wang, L.; Sun, J.; Wang, W.; Qi, X.; Wang, F. Bayesian network analysis model on landing exceedance risk based on flight OAR data. J. Saf. Environ. 2023, 23, 26–34.
  22. Wang, L.; Wu, C.; Sun, R. An analysis of flight Quick Access Recorder (QAR) data and its applications in preventing landing incidents. Reliab. Eng. Syst. Saf. 2014, 127, 86–96.
  23. Wang, L.; Gao, S.; Hong, R.; Jiang, Y. Effects of age and flight exposure on flight safety performance: Evidence from a large cross-sectional pilot sample. Saf. Sci. 2023, 165, 106199.
  24. Zhang, N.; Zhao, D.; An, J.; Wang, L. A Method of Analyzing Flight Exceedance Events Based on Association Rule and QAR Data. In Proceedings of the 2023 7th International Conference on Transportation Information and Safety (ICTIS), Xi’an, China, 4–6 August 2023; pp. 2249–2252.
  25. Li, X.; Shang, J.; Zheng, L.; Wang, Q.; Liu, D.; Liu, X.; Li, F.; Cao, W.; Sun, H. IMTCN: An Interpretable Flight Safety Analysis and Prediction Model Based on Multi-Scale Temporal Convolutional Networks; IEEE Transactions on Intelligent Transportation Systems: New York, NY, USA, 2023.
  26. Chen, N.; Man, Y.; Li, J. Risk Assessment Method of Civil Aircraft Approach and Landing at High Plateau Based on QAR Data. J. Beijing Univ. Aeronaut. Astronaut. 2022.
  27. Westphal, P.; Wagner, O. Flight accident prevention with hazard priority based realtime risk reduction. In Proceedings of the 19th AIAA Applied Aerodynamics Conference, Anaheim, CA, USA, 11–14 June 2001; p. 1319.
  28. Nie, L.; Huang, S.; Shu, P.; Wang, X. Intelligent Diagnosis for Hard Landing of Aircraft Based on SVM. China Saf. Sci. J. 2009, 19, 5.
  29. Gong, S.; Huang, S. Implementation of Flight Data Quick Decoding System. Sci. Technol. Eng. 2010, 10, 5076–5081.
  30. Zhao, Y.; Sun, H. Research on hybrid model for aircraft flight state prediction. N. A. Dynam. 2016, 34, 81–85+89.
  31. Lu, H.; Deng, X. Real-time Flight Trajectory Security Monitoring Technology Based-on ACARS. Aircr. Des. 2009, 29, 52–56.
  32. Fu, M.; Chen, J. LSTM Model-Based Flight Trajectory Prediction for Airport Flights. Mod. Comput. 2022, 28, 55–60.
  33. Qian, L.; Wang, W. A C-GRU based flight trajectory prediction method. Electron. Meas. Technol. 2022, 45, 87–92.
  34. Wang, Q.; Wu, K.; Zhang, T.; Kong, Y.; Qian, W. Aerodynamic Modeling and Parameter Estimation from QAR Data of an Airplane Approaching a High-altitude Airport. Chin. J. Aeronaut 2012, 25, 361–371.
  35. Korsun, O.; Poplavskii, B.K. Estimation of systematic errors of onboard measurement of angle of attack and sliding angle based on integration of data of satellite navigation system and identification of wind velocity. J. Comput. Syst. Sci. Int. 2011, 50, 130–143.
  36. Sun, R.; Han, W. Analysis on paremeters characteristics of flight exceedance events based on distinction test. J. Saf. Sci. Technol. 2011, 7, 22–27.
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Subjects: Ergonomics
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Feiyin Wang
,
Jintong Yuan
,
Xiaochen Liu
,
Pengtao Wang
,
Mao Xu
,
Xiaoyu Li
,
Hang Li
View Times: 96
Revisions: 2 times (View History)
Update Date: 07 Feb 2024
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