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Comparative Analyses of Operational Flights
This report describes a cooperative experiment conducted by ONERA and NASA, with the support of Airbus S.A.S. and easyJet Airline Company, Ltd. The study evaluated the benefits of two distinctly different methodologies for analyzing the same set of digital flight-recorded data. The experiment analyzed a set of easyJet commercial-flight data with both typical Flight Operational Quality Assur-ance (FOQA) software of an airline (in this case, AirFASE, developed by Airbus and Teledyne) and The Morning Report of Atypical Flights (developed by NASA). The study demonstrated the feasibility and potential value of using The Morning Report tool in conjunction with the FOQA airline tool and also showed the complementarities of the results produced by the two approaches.
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Comparison of Algorithms for Anomaly Detection in Flight Recorder Data of Airline Operations
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Published at 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSM 17 - 19 September 2012, Indianapolis, Indiana
국토교통부 항공여객 이동특성 조사
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항공여객 이동 특성 조사는 연도별 항공여객 이동 특성을 조사하여 노선별 보고서, 종합 분석 보고서 등을 다운로드할 수 있습니다.
ANALYZING AVIATION SAFETY REPORTS: FROM TOPIC MODELING TO SCALABLE MULTI-LABEL CLASSIFICATION
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ANALYZING AVIATION SAFETY REPORTS: FROM TOPIC MODELING TO SCALABLE MULTI-LABEL CLASSIFICATION AMRUDIN AGOVIC*, HANHUAI SHAN*, AND ARINDAM BANERJEE* Abstract. The Aviation Safety Reporting System (ASRS) is used to collect voluntarily submitted aviation safety reports from pilots, controllers and others. As such it is particularly useful in researching aviation safety deficiencies. In this paper we address two challenges related to the analysis of ASRS data: (1) the unsupervised extraction of meaningful and interpretable topics from ASRS reports and (2) multi-label classification of ASRS data based on a set of predefined categories. For topic modeling we investigate the practical usefulness of Latent Dirichlet Allocation (LDA) when it comes to modeling ASRS reports in terms of interpretable topics. We also utilize LDA to generate a more compact representation of ASRS reports to be used in multi-label classification. For multi-label classification we propose a novel and highly scalable multi-label classification algorithm based on multi-variate regression. Empirical results indicate that our approach is superior to several baseline and state-of-the-art approaches.
Sample Report
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Sample report in support of "Discovering Anomalous Aviation Safety Events Using Scalable Data Mining Algorithms" manuscript.
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