SDTAN: Scalable Deep Time-Aware Attention Network for Interpretable Hard Landing Prediction

Chen, Haodong; Shang, Jiaxing; Zheng, Linjiang; Li, Xu; Liu, Xiaodong; Sun, Hong; Zhao, Xinbin; Yu, Lei

doi:10.1109/tits.2023.3267035

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…Recently, Li et al [31,32] performed automatic classification and identification for the causes of hard landing using the K-means clustering algorithm. Chen et al [33] proposed a deep learning neural network model with time-aware attention for interpretable hard landing prediction. Jin et al [34] 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.…”

Section: Other Qar-based Flight Safety Studiesmentioning

confidence: 99%

A Runway Overrun Risk Assessment Model for Civil Aircraft Based on Quick Access Recorder Data

Li,

Zhang,

Shang

et al. 2023

Applied Sciences

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The quick access recorder (QAR), as an airborne device used to monitor and record flight parameters, has been widely installed on various types of aircraft. Based on QAR data, research on runway overrun, a typical flight safety incident, has attracted widespread attention in recent years. However, existing runway overrun risk models generally suffer from oversimplified risk metrics or insufficient consideration of risk dynamics. In this paper, we propose a new dynamic runway overrun risk assessment model based on QAR data. We first consider the noise of aircraft trajectory data in the QAR parameters and present a landing trajectory correction method combining ground speed and runway position information. Second, to improve the accuracy of the risk assessment model, we design an algorithm to automatically recognize the aircraft autobrake level during the landing phase, based on which a new dynamic risk assessment model is developed. Finally, feature engineering is performed to extract the relevant contributing factors of runway overrun risk, based on which classification and regression models are applied to identify risky flights and predict the risk values. The proposed risk assessment model was evaluated using QAR data from an airline in China. The results show that the automatic deceleration rate, the way that the aircraft approaches the runway, the touchdown distance, and the kinetic energy at 50 ft are key factors in the risk of runway overrun during the landing phase.

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