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Topological Methods for Flight Trajectory Analysis and Planned Path Optimization
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AI
DOI：10.1142/s1793005728500457.png)
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Abstract
En 中文
Flight trajectories are typically analyzed using a single reference path, although aircraft rarely follow the same route across different days or seasons due to meteorological and operational factors. This limits the ability of conventional methods to capture systematic and season-dependent variations in flight behavior. To address this limitation, this study applies Topological Data Analysis (TDA) to the trajectories of flight KL1613 between Amsterdam and Istanbul over the period from February 2023 to January 2024. The proposed framework integrates geometric measures, including the Haversine distance, with topological descriptors such as persistence diagrams and persistence landscapes to characterize structural deviations in flight paths. The analysis identifies recurrent one-dimensional topological features corresponding to alternative routing patterns and seasonal variations. Monthly trajectory data are clustered into two dominant groups based on combined geometric and topological similarity, from which representative reference paths are extracted for each month. Cost analysis shows that the proposed monthly reference trajectories consistently reduce fuel consumption and travel time compared to both the planned annual reference route and the actual flown trajectories. These results demonstrate the effectiveness of TDA for capturing seasonal structure in flight trajectories and its potential applicability to large-scale air traffic analysis.
Keywords:
Topological data analysis
persistent homology
flight trajectories
route optimization
Journal
N
IF:
1.1
Papers:
83
Citations:
0