A Pilot Recommender System using K-Means Clustering to Find Desirable Paths in Aircraft Takeoffs
Nurmohamad
Nurmohamad, Joint director of income tax, Ministry of finance, Department of revenue, Government of India.
Manuscript received on January 09, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on January 30, 2020. | PP: 2761-2764 | Volume-8 Issue-5, January 2020. | Retrieval Number: D7728118419/2020©BEIESP | DOI: 10.35940/ijrte.D7728.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Takeoff paths, K-Means clustering, Turn angle, geographic distance and outliers.
Scope of the Article: Classification.