A Pilot Recommender System using K-Means Clustering to Find Desirable Paths in Aircraft Takeoffs

Abstract

This paper aims to study the geometric patterns produced by aircrafts on different takeoff paths and to establish a correlation between the fuel consumption and the geometry of the path. Based on the findings of the study, pilots could be advised to change their flying styles and strategy during take-off on a path that maximizes fuel conservation. This is validated by grouping the similar takeoff paths using the k-means clustering technique and by verifying linear relationship between the parameters of different paths in the clusters and their corresponding braking patterns. In addition, various runways are classified in order to study the variations in the takeoff paths.