A Flight Process Importance Framework for Evaluating Pilot Performance During Airplane Landing

Aviation accidents are frequently related to pilots’ operations, especially during a landing phase. Therefore, accurately evaluating a pilot’s performance during this phase is crucial for minimizing landing risks. Traditional assessment methods, however, primarily focus on discrete monitoring points, failing to capture the continuous and dynamic nature of a pilot’s performance throughout the entire landing phase. To address this issue, we propose a Flight Process Importance (FPI) assessment framework that precisely determines accurate landing timing and captures the diverse operational characteristics of pilots. It consists of two components: Time-varying Importance Coefficient (TIC) and Pilot Characteristics Matrix (PCM). TIC develops a Spatio-Temporally Consistent Attention Network (STCAN) to classify Quick Access Recorder (QAR) data for anomalous event detection. It then determines the importance of different periods during the landing process by analyzing the STCAN model’s response to the data in an interpretable manner. PCM generates a parameter matrix for each flight by deriving the ideal intervals of various parameters through the interquartile range. This matrix is used to identify the duration and intensity of anomalies in operations across different pilots. By integrating TIC and PCM, our framework computes an evaluation matrix for each flight, quantifying the operational risk factors associated with pilots. Experimental results indicate that STCAN significantly surpasses other algorithms on QAR data. FPI provides a more precise and comprehensive assessment of a pilot’s performance. In particular, our findings highlight that the 10 seconds before landing to the touchdown are the most critical period of airplane landing.

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Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2025-04-18
DOI
https://doi.org/10.1109/tits.2025.3558469
Citations
1
Primary Topic
Air Traffic Management and Optimization
Type
article
Field-Weighted Citation Impact
4.99

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article

A Flight Process Importance Framework for Evaluating Pilot Performance During Airplane Landing

Jinfeng Yang, Zhenhua Huang, Jin Ren, Shunzhi Yang et al.
1 citations
IEEE Transactions on Intelligent Transportation Systems
Air Traffic Management and Optimization
4.99
article

A Flight Process Importance Framework for Evaluating Pilot Performance During Airplane Landing

Jinfeng Yang, Zhenhua Huang, Jin Ren, Shunzhi Yang, MengChu Zhou, Xiong Yang
article en
1 citations

Abstract

Aviation accidents are frequently related to pilots’ operations, especially during a landing phase. Therefore, accurately evaluating a pilot’s performance during this phase is crucial for minimizing landing risks. Traditional assessment methods, however, primarily focus on discrete monitoring points, failing to capture the continuous and dynamic nature of a pilot’s performance throughout the entire landing phase. To address this issue, we propose a Flight Process Importance (FPI) assessment framework that precisely determines accurate landing timing and captures the diverse operational characteristics of pilots. It consists of two components: Time-varying Importance Coefficient (TIC) and Pilot Characteristics Matrix (PCM). TIC develops a Spatio-Temporally Consistent Attention Network (STCAN) to classify Quick Access Recorder (QAR) data for anomalous event detection. It then determines the importance of different periods during the landing process by analyzing the STCAN model’s response to the data in an interpretable manner. PCM generates a parameter matrix for each flight by deriving the ideal intervals of various parameters through the interquartile range. This matrix is used to identify the duration and intensity of anomalies in operations across different pilots. By integrating TIC and PCM, our framework computes an evaluation matrix for each flight, quantifying the operational risk factors associated with pilots. Experimental results indicate that STCAN significantly surpasses other algorithms on QAR data. FPI provides a more precise and comprehensive assessment of a pilot’s performance. In particular, our findings highlight that the 10 seconds before landing to the touchdown are the most critical period of airplane landing.

IEEE Transactions on Intelligent Transportation SystemsVol. 26(7)
Macau University of Science and Technology (MO), Shenzhen Polytechnic University (CN), South China Normal University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 6%
Air Traffic Management and Optimization
4.99
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