Understanding the Incidents on Legacy Airlines with Machine Learning - Case Study - Top 5 US Airlines

Aug 3, 2026

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Machine-learning analysis of incidents in legacy airline operations focuses on detecting anomalous aircraft behavior, assessing safety, and quantifying associated risk. The study motivates modeling minor accidents to reduce substantial industry costs, stabilize margins, and improve consumer confidence. Existing research on anomaly detection, causal and human-error models, and hybrid SVM/DNN approaches is reviewed, while a key gap is noted regarding incident damage type prediction and impacting variables.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Background

Literature

Main Relevant Research Problems

Methodology

Logistic Regression

Deep Learning

Support Vector Machines

Results

Model Performance Metrics

Understanding the Incidents on Legacy Airlines with Machine Learning: Case Study Top 5 US Airlines Dean Sutherland Dr. Burak Cankaya, ERAU Contact: [bcankaya@erau.edu](bcankaya@erau.edu) for questions References: Anbil R., Tanga R., Johnson E. L.A global approach to crew-pairing optimization, IBM Syst. J., 31 (1) (1992), pp. 71-78, Elsayed S. M., Sarker R.A., Essam D. L.A new genetic algorithm for solving optimization problems, Eng. Appl. Artif. Intell., 27 (2014), pp. 57-69, Bazargan M.Airline Operations and Scheduling, (second ed. ), Ashgate Publishing Ltd. (2004-2010) Discovery Day 2023 Interpreting Understanding the Incidents on Legacy Airlines with Machine Learning: Case Study Top 5 US Airlines Dean Sutherland Dr. Burak Cankaya, ERAU Background 􀂃 The air transportation system is part of most nations critical infrastructure 􀂃 Rigorous safety standards are effective; however minor accidents/incidents are somewhat frequent in comparison to major accidents even with standards in place 􀂃 There are known patterns pilots see anecdotally; but the data must be analyzed 􀂃 Incident have a substantial cost to the airlines, raise ticket prices, erode consumer confidence, and generate insurance claims 􀂃 Modeling minor accidents to identify causal and contributing data stands to save the industry substantial costs and increase margins Literature Main Relevant Research Problems qualitative/quantitative approaches to detect anomalous aircraft behavior (Hwang et al, 2008) assess the safety, and quantify the risk associated causal models, collision risk models, human error models, and third-party risk models (Netjasov and Janic, 2008 ) automatically detect flight trajectory anomalies (Di Ciccio et al, 2016) A hybrid model blending SVM and DNN ensemble prediction for aviation incidents (Zhang et al. 2019) Gap: Predicting Incident Damage Type and Impacting Variables is not addressed Methodology 􀂃 Logistic Regression 􀂃 Deep Learning 􀂃 Support Vector Machines Results | | | Method Accuracy | | | --- | --- | --- | --- | | | Deep Learning | Logistic Regression | SVM | | Accuracy | 99.4 | 97.1 | 100 | | Class Recall | 92.7 | 64.4 | 100 | | AUC | 0.99 | 0.87 | 1 | Results Results | Criterion | Value | Standard Deviation | | --- | --- | --- | | accuracy | 0.994 | 0.003 | | classification_error | 0.006 | 0.003 | | AUC | 0.991 | 0.014 | | precision | 1.0 | 0.0 | | recall | 0.928 | 0.041 | | f_measure | 0.962 | 0.022 | | sensitivity | 0.928 | 0.041 | | specificity | 1.0 | 0.0 |

FAQs

Why is analyzing minor airline incidents important in the study?

The study notes that incidents, even minor ones, occur with some frequency and impose substantial costs on airlines, including higher ticket prices, reduced consumer confidence, and insurance claims.

What modeling approaches are used to study incidents?

The methodology includes Logistic Regression, Deep Learning, and Support Vector Machines to detect patterns and evaluate incident-related risk.

How well do the models perform according to the results?

Results report very high accuracy for Deep Learning (about 99.4%) and Logistic Regression (about 97.1%), with SVM showing 100% accuracy; AUC values range from roughly 0.87 to 1.0 depending on the method.

Aug 3, 2026

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