The Marriage of Flight Data and Machine Learning to Improve Aviation Safety — A Case of Long Landing
By: Kevin Choy
Tags: Airlines, Aviation Safety, Data Science, Data Visualization, Machine Learning

The Marriage of Flight Data and Machine Learning to Improve Aviation Safety — A Case of Long Landing Kevin Choy Nov 28, 2021·4 min read

Aviation safety is a big talk in the airline industry. According to Airbus (Airbus S.A.S., 2020), just the landing phase alone contributed between 1999–2019. In the same period, 25% of fatal accidents are runway excursion and undershoot and overshoot related. Hence, aviation safety remains a concern for airlines even throughout the COVID19 pandemic. But can machine learning help the industry in this area?

In my previous role, I had the opportunity to conduct a descriptive analysis on long landings using flight data. An Airbus A350 aircraft (Airbus, n.d.) has more than 50,000 sensors and collected more than 2 terabytes of data daily. These data are valuable. Every airport runway has a different length, but there is a ‘sweet spot’ for pilots to land, known as the runway, aim point. Landing further into the runway means a lesser remaining runway for the aircraft to slow down. Therefore, I was keen to identify the factors contributing to long landing. Due to company proprietary data, the descriptive analysis will focus on my analysis process and tools used.

Image by Pete Linforth from Pixabay

Sixty-eight independent variables were identified and collected from the aircraft flight data and pilot duty hours for over 30,000 flights. The flight data were categorized into aircraft systems, flight control, and environment categories. Pilot duty hours could shed some light if pilot fatigue contributes to their flight performance in long landings. The performance metric used was the actual point where the aircraft touched down on the runway. Data were cleaned through a round of exploratory data analysis using the Pandas Profiling tool. Features with a single distinct value were dropped from the analysis. Features with missing values were computed using mean for numerical features and constant for categorical. If you are interested in the source code, you can find it at my GitHub here.

PyCaret was used for model training and selection. It helps to find the best regression model from the multiple regression models. For this analysis, the best model was defined by the highest explainability (R2) and minimal errors (MAPE). For consistency of output, three sets of heights of data (i.e., 1,000, 500, and 50 feet) were used to cross-validate the result of each model. To improve the model, it was tuned and ensemble. The result of the best model provides the explainability, error and important independent variables that contribute to the performance metric.

For visual presentation to stakeholders, Tableau Desktop was used for data visualization in which the performance metric was benchmarked against the independent variable. The performance metric was plotted against important independent variables using a scatter plot to observe the density of both variables. A bar chart with high cases of long landings at the airport was presented to show the concerning airports.

Image by ysn Benz from Pixabay

While correlation is not causality, the outcome of the analysis supported and facilitated the discussion with the stakeholders, such as management pilots, crew resource managers, and safety pilots, to establish root causes and recommendations. The cause of long landing can be one of the dirty dozens of human factors. The ‘dirty dozen’ is one of the human errors committed that can lead to incidents and accidents (Skybrary, n.d.). If so, training or procedural review can be recommended to address human factors to improve safe flight operations. Furthermore, the outcome may warrant deep dive into the area of interest to gain more insights into certain issues that are not in the scope of the analysis.

Although this is a simple descriptive analysis using machine learning, the machine learning potential to use flight data to discover “hidden” factors that can bring fresh insights into the common safety risk. Another use case for machine learning in this domain is to identify anomalies from any of the independent variables and identify safety hazards. With many great machine learning and data visualization tools out there, there is virtually no limit to what we can do with flight data to promote aviation safety.

References

Airbus. (n.d.). Data revolution in aviation. Retrieved October 24, 2021, from Airbus: https://www.airbus.com/public-affairs/brussels/our-topics/innovation/data-revolution-in-aviation.html

Airbus S.A.S. (2020). A Statistical Analysis of Commercial Aviation Accidents 1958–2019. Blaganc Cedex: Airbus. Retrieved from https://www.airbus.com/content/dam/corporate-topics/publications/safety-first/Statistical-Analysis-of-Comercial-Aviation-Accidents-1958-2019.pdf

Skybrary. (n.d.). The Human Factors “Dirty Dozen”. Retrieved November 28, 2021, from Skybrary: https://skybrary.aero/articles/human-factors-dirty-dozen