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Li, X.; Zhang, L.; Shang, J.; Li, X.; Qian, Y.; Zheng, L. Flight Safety Assessment Based on Quick Access Recorder. Encyclopedia. Available online: https://encyclopedia.pub/entry/49325 (accessed on 05 December 2025).
Li X, Zhang L, Shang J, Li X, Qian Y, Zheng L. Flight Safety Assessment Based on Quick Access Recorder. Encyclopedia. Available at: https://encyclopedia.pub/entry/49325. Accessed December 05, 2025.
Li, Xiuyi, Lin Zhang, Jiaxing Shang, Xiaoquan Li, Yu Qian, Linjiang Zheng. "Flight Safety Assessment Based on Quick Access Recorder" Encyclopedia, https://encyclopedia.pub/entry/49325 (accessed December 05, 2025).
Li, X., Zhang, L., Shang, J., Li, X., Qian, Y., & Zheng, L. (2023, September 18). Flight Safety Assessment Based on Quick Access Recorder. In Encyclopedia. https://encyclopedia.pub/entry/49325
Li, Xiuyi, et al. "Flight Safety Assessment Based on Quick Access Recorder." Encyclopedia. Web. 18 September, 2023.
Flight Safety Assessment Based on Quick Access Recorder
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This entry is adapted from the peer-reviewed paper 10.3390/app13179828

The QAR (quick access recorder), as an airborne data recorder, can collect and store multidimensional flight parameter data during a flight. Currently, QAR devices have been widely installed on various types of aircraft. It has been proven by practical experience that the QAR can effectively collect thousands of real-time parameters during flight. These flight parameters can reflect the condition of the external environment, flight attitude, aircraft status, pilot operations, etc. Therefore, they provide objective evidence for flight technical evaluations, safety incident investigations, and the elimination of potential flight safety hazards, which can yield better flight safety.

flight safety QAR data runway overrun risk assessment model machine learning

1. Introduction

Flight safety is a primary focus of the civil aviation industry [1][2]. According to Boeing’s statistical summary of commercial jet airplane accidents from 1959 to 2018 [3], the approach and landing are the flight phases during which safety incidents are most likely to occur. The landing phase only accounts for an average of 1% of the total flight time; however, the occurrence rate of flight incidents for this phase is as high as 24%. Therefore, the landing phase is a critical phase for ensuring flight safety.
Runway overrun is a flight safety incident in which an aircraft fails to slow down in time after landing, resulting in it running off of the runway. This may cause serious economic losses to airlines and even endanger the lives of passengers in severe cases. According to the runway safety accident analysis report of the International Air Transport Association (IATA) [4] published in 2015, runway overrun incidents accounted for 44% of the 78 runway excursion incidents that occurred from 2010 to 2014, and they accounted for 100% of the five fatal accidents. Therefore, it is crucial to study the risk of runway overrun for ensuring civil aviation safety.
Regarding the risk of runway overrun for commercial aircraft, existing research can be broadly categorized into two types. The first type of research focuses on post hoc analysis of runway overrun incidents based on historical accident data [5][6][7][8][9]. These studies mainly employ probabilistic statistical analysis methods, combined with accident investigation reports, to analyze the causes of runway overrun accidents and propose corresponding improvement measures. The main problem with this type of research is that there is a limited number of accident data samples. Therefore, it is difficult to fully explore valuable information within vast QAR data, thus leading to significant limitations. The second type of research focuses on flights where runway overrun incidents did not occur and uses QAR data to analyze the relative risk levels of different flights. The main issue with this type of research is that the risk assessment metrics are oversimplistic and fail to consider the dynamic runway overrun risk due to the pilot’s deceleration operation after touchdown.

2. Runway Overrun

As mentioned above, current studies on the risk of runway overrun can be roughly divided into two categories, i.e., studies based on real historical accident data and studies based on QAR data. In this section, a brief overview of the related work in these two categories is given.
For studies based on historical accident data, Kirkland et al. [5] attempted to normalize historical accident data to facilitate future research. They further adopted bivariate analysis to build a probabilistic model for the risk factors of runway overrun incidents [6]. Their model mainly considers factors such as the aircraft weight, tailwind, light conditions, weather, approach speed, and touchdown point. Valdés et al. [7] added factors such as the aircraft type, airport elevation, and safety area length to their probabilistic model. Ayres et al. [8] built a probabilistic model that considers the spatial distribution of accident locations to describe runway overrun and excursion accidents. Wagner et al. [9] studied the severity of accident consequences using logistic regression and Bayesian logistic regression methods based on over 1400 runway overrun and excursion data from an ACRP database from 1970 to 2009, which included five types of accidents: runway overrun and runway excursion in both takeoff and landing phases and undershoot.
For studies based on QAR data, machine learning and risk assessment models are mainly employed to analyze QAR data, thereby identifying key factors contributing to the runway overrun risk. Wang et al. [10][11] analyzed long landing incidents with a risk of runway overrun and used variance analysis and linear regression (LR) methods based on QAR data to analyze different factors of long landing. Subsequently, Wang et al. [12][13] proposed a runway overrun risk assessment model based on QAR data, defining the long landing risk as the product of the probability of a certain landing distance and the severity of risk corresponding to that landing distance. Kang et al. [14][15] investigated the long landing problem and proposed a deep sequence-to-sequence model to predict the landing speed and distance. Lv et al. [16] defined runway overrun risk as a function related to the remaining runway distance and touchdown speed, dividing flights into high-risk and low-risk flights based on the magnitude of the risk indicator. They ultimately employed machine learning algorithms for high-risk flight classification. Ayra et al. [17] took the remaining runway distance when the aircraft’s ground speed reaches 80 knots as a measure of runway overrun risk. They used Bayesian networks to analyze the influencing factors of runway overrun risk, including the crosswind, tailwind, surface contamination, approach mode, autobrake usage, and entry altitude. To address the problem of a lack of positive samples for runway overrun, Koppitz et al. [18] employed subset simulation methods to calculate the changes in the probability of runway overrun incidents based on selected factor distributions, thereby identifying relevant risk factors.
The limitations of the existing studies are as follows: For studies based on historical accident data, they can only provide coarse-grained information, such as the weather condition, aircraft weight, aircraft age, etc. Therefore, compared to QAR data, the available information from historical accident data are very limited, and it is difficult to uncover valuable in-flight information to support flight safety analysis. For studies based on QAR data, the current risk assessment metrics are usually oversimplistic and they failed to consider the dynamic runway overrun risk of the pilot’s deceleration operation after touchdown.

3. Other QAR-Based Flight Safety Studies

In addition to studying runway overrun, some other studies have been performed regarding flight safety issues based on QAR data, among which the main category is exceedance events. Qi et al. [19] proposed a new method for partitioning the risk subspaces of exceedance events based on rough set theory and a particle swarm multiobjective optimization algorithm. Liu et al. [20] developed a risk assessment model for exceedance events, defining exceedance risk as the product of the probability of exceedance events and the severity of the events. They developed a quality assessment system for pilot operations based on their model. Wang et al. [21] investigated the relationship between the risk cognition of pilots and exceedance events based on QAR. Li et al. [22] investigated the tail strike risk and proposed an unsupervised learning method to discover patterns of unsafe pilot stick operations during the landing stage.
Hard landing is another typical flight safety incident that researchers are concerned about. Hu et al. [23] designed a prediction model based on a support vector machine (SVM) for hard landing. Qiao et al. [24] employed RBF neural networks and the K-means clustering algorithm to predict hard landing. Tong et al. [25] addressed the problem of hard landing using a deep learning framework. Considering the temporal characteristics of QAR data, they proposed a hard landing prediction framework based on long short-term memory (LSTM) networks. Additionally, they applied LSTM networks to predict aircraft landing speeds [26]. Chen et al. [27] used scalar measurements and aggregated QAR data to detect the influential features of hard landing. Recently, Li et al. [28][29] performed automatic classification and identification for the causes of hard landing using the K-means clustering algorithm. Chen et al. [30] proposed a deep learning neural network model with time-aware attention for interpretable hard landing prediction. Jin et al. [31] developed transfer learning methods for high-dimensional quantile regression and applied the methods to solve the problem of determining the hard-landing risk for flight safety.
In terms of abnormal flight analysis, Li et al. [32][33][34] applied clustering and outlier detection methods to identify abnormal flights from massive QAR data. Later, Li et al. [35] improved their model to detect the specific flight phase of abnormal flights in which the QAR parameters deviate from their normal behavior.

References

  1. Lu, X.; Chen, D.; Zhang, F. Grey correlation analysis of hard landing based on QAR data. In Proceedings of the 2019 IEEE 1st International Conference on Civil Aviation Safety and Information Technology (ICCASIT), Kunming, China, 17–19 October 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 79–83.
  2. Xie, J.; Sun, H.; Jiao, Y.; Lu, B. Association rules mining with QAR data: An analysis on unstable approaches. In Proceedings of the 2019 IEEE 1st International Conference on Civil Aviation Safety and Information Technology (ICCASIT), Kunming, China, 17–19 October 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 603–606.
  3. Boeing, C.A. Statistical Summary of Commercial Jet Airplane Accidents-Worldwide Operations 1959–2018. 2019. Available online: http://www.boeing.com/news/techissues (accessed on 1 October 2021).
  4. IATA. IATA Runway Safety Accident Analysis Report 2010–2014; IATA: Montreal, QC, Canada, 2015.
  5. Kirkland, I.; Caves, R.; Hirst, M.; Pitfield, D. The normalisation of aircraft overrun accident data. J. Air Transp. Manag. 2003, 9, 333–341.
  6. Kirkland, I.; Caves, R.; Humphreys, I.; Pitfield, D. An improved methodology for assessing risk in aircraft operations at airports, applied to runway overruns. Saf. Sci. 2004, 42, 891–905.
  7. Valdés, R.M.A.; Comendador, F.G.; Gordún, L.M.; Nieto, F.J.S. The development of probabilistic models to estimate accident risk (due to runway overrun and landing undershoot) applicable to the design and construction of runway safety areas. Saf. Sci. 2011, 49, 633–650.
  8. Ayres, M., Jr.; Shirazi, H.; Carvalho, R.; Hall, J.; Speir, R.; Arambula, E.; David, R.; Gadzinski, J.; Caves, R.; Wong, D.; et al. Modelling the location and consequences of aircraft accidents. Saf. Sci. 2013, 51, 178–186.
  9. Wagner, D.C.; Barker, K. Statistical methods for modeling the risk of runway excursions. J. Risk Res. 2014, 17, 885–901.
  10. Wang, L.; Wu, C.; Sun, R. Pilot operating characteristics analysis of long landing based on flight QAR data. In Proceedings of the Engineering Psychology and Cognitive Ergonomics. Applications and Services: 10th International Conference, EPCE 2013, Held as Part of HCI International 2013, Las Vegas, NV, USA, 21–26 July 2013; Proceedings, Part II 10. Springer: Berlin/Heidelberg, Germany, 2013; pp. 157–166.
  11. 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.
  12. Wang, L.; Ren, Y.; Sun, H.; Dong, C. A landing operation performance evaluation system based on flight data. In Proceedings of the Engineering Psychology and Cognitive Ergonomics: Cognition and Design: 14th International Conference, EPCE 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, 9–14 July 2017; Proceedings, Part II 14. Springer: Berlin/Heidelberg, Germany, 2017; pp. 297–305.
  13. Wang, L.; Zhang, J.; Dong, C.; Sun, H.; Ren, Y. A method of applying flight data to evaluate landing operation performance. Ergonomics 2019, 62, 171–180.
  14. Kang, Z.; Shang, J.; Feng, Y.; Zheng, L.; Liu, D.; Qiang, B.; Wei, R. A Deep Sequence-to-Sequence Method for Aircraft Landing Speed Prediction Based on QAR Data. In Proceedings of the International Conference on Web Information Systems Engineering, Biarritz, France, 31 October–3 November 2020; pp. 516–530.
  15. Kang, Z.; Shang, J.; Feng, Y.; Zheng, L.; Wang, Q.; Sun, H.; Qiang, B.; Liu, Z. A deep sequence-to-sequence method for accurate long landing prediction based on flight data. IET Intell. Transp. Syst. 2021, 15, 1028–1042.
  16. 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.
  17. Ayra, E.S.; Ríos Insua, D.; Cano, J. Bayesian network for managing runway overruns in aviation safety. J. Aerosp. Inf. Syst. 2019, 16, 546–558.
  18. Koppitz, P.; Wang, C.; Höhndorf, L.; Sembiring, J.; Wang, X.; Holzapfel, F. From raw operational flight data to incident probabilities using subset simulation and a complex thrust model. In Proceedings of the AIAA Scitech 2019 Forum, San Diego, CA, USA, 6–9 January 2019; p. 2233.
  19. Xueyan, S.; Mingliang, Q.; Mingang, G. Safety risk analysis in flight operations quality assurance. Syst. Eng. Procedia 2012, 5, 81–86.
  20. Liu, S.; Zhang, Y.; Chen, J. A system for evaluating pilot performance based on flight data. In Proceedings of the Engineering Psychology and Cognitive Ergonomics: 15th International Conference, EPCE 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, 15–20 July 2018; Proceedings 15. Springer: Berlin/Heidelberg, Germany, 2018; pp. 605–614.
  21. Wang, L.; Zhang, J.; Sun, H.; Ren, Y. Risk cognition variables and flight exceedance behaviors of airline transport pilots. In Proceedings of the Engineering Psychology and Cognitive Ergonomics: 15th International Conference, EPCE 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, 15–20 July 2018; Proceedings 15. Springer: Berlin/Heidelberg, Germany, 2018; pp. 725–737.
  22. Li, X.; Qian, Y.; Chen, H.; Zheng, L.; Wang, Q.; Shang, J. An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data. Appl. Sci. 2022, 12, 12789.
  23. Hu, C.; Zhou, S.H.; Xie, Y.; Chang, W.B. The study on hard landing prediction model with optimized parameter SVM method. In Proceedings of the 2016 35th Chinese Control Conference (CCC), Chengdu, China, 27–29 July 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 4283–4287.
  24. Qiao, X.; Chang, W.; Zhou, S.; Lu, X. A prediction model of hard landing based on RBF neural network with K-means clustering algorithm. In Proceedings of the 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia, 4–7 December 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 462–465.
  25. Tong, C.; Yin, X.; Li, J.; Zhu, T.; Lv, R.; Sun, L.; Rodrigues, J.J. An innovative deep architecture for aircraft hard landing prediction based on time-series sensor data. Appl. Soft Comput. 2018, 73, 344–349.
  26. Tong, C.; Yin, X.; Wang, S.; Zheng, Z. A novel deep learning method for aircraft landing speed prediction based on cloud-based sensor data. Future Gener. Comput. Syst. 2018, 88, 552–558.
  27. Chen, R.; Jin, J. Detection of Influential Features of Hard Landing Based on QAR Data. In Proceedings of the 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT), Dali, China, 11–13 October 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 526–529.
  28. Li, X.; Shang, J.; Zheng, L.; Liu, D.; Qi, L.; Liu, L. CurveCluster: Automated recognition of hard landing patterns based on QAR curve clustering. In Proceedings of the IEEE International Conference on Ubiquitous Intelligence and Computing, Leicester, UK, 19–23 August 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 602–609.
  29. Li, X.; Shang, J.; Zheng, L.; Wang, Q.; Sun, H.; Qi, L. Curvecluster+: Curve clustering for hard landing pattern recognition and risk evaluation based on flight data. IEEE Trans. Intell. Transp. Syst. 2022, 23, 12811–12821.
  30. Chen, H.; Shang, J.; Zheng, L.; Li, X.; Liu, X.; Sun, H.; Zhao, X.; Yu, L. SDTAN: Scalable Deep Time-Aware Attention Network for Interpretable Hard Landing Prediction. IEEE Trans. Intell. Transp. Syst. 2023.
  31. Jin, J.; Yan, J.; Chen, K. Transfer learning with quantile regression. arXiv 2022, arXiv:2212.06693.
  32. Li, L.; Gariel, M.; Hansman, R.J.; Palacios, R. Anomaly detection in onboard-recorded flight data using cluster analysis. In Proceedings of the 2011 IEEE/AIAA 30th Digital Avionics Systems Conference, Seattle, WA, USA, 16–20 October 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 4A4-1–4A4-11.
  33. Das, S.; Li, L.; Srivastava, A.; Hansman, R.J. Comparison of algorithms for anomaly detection in flight recorder data of airline operations. In Proceedings of the 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Indianapolis, IN, USA, 17–19 September 2012; p. 5593.
  34. Li, L.; Das, S.; John Hansman, R.; Palacios, R.; Srivastava, A.N. Analysis of flight data using clustering techniques for detecting abnormal operations. J. Aerosp. Inf. Syst. 2015, 12, 587–598.
  35. Li, L.; Hansman, R.J.; Palacios, R.; Welsch, R. Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring. Transp. Res. Part C Emerg. Technol. 2016, 64, 45–57.
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Xiuyi Li
, Lin Zhang , Jiaxing Shang , Xiaoquan Li , Yu Qian , Linjiang Zheng
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Update Date: 18 Sep 2023
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