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14 February 2024 Analysis of flight operation characteristics based on DBSCAN and K-Means
Xiao Huang, Yong Tian, Kexin Niu, Jiangchen Li
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Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 1301856 (2024) https://doi.org/10.1117/12.3024759
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China
Abstract
With the increasing complexity of air traffic, the operational characteristics of flights remain largely unexplored. In particular, the revision of Scheduled Flight Block Time (SFBT) heavily relies on statistical analysis of historical data. Therefore, the objective of this paper is to propose a method for analyzing flight operation characteristics from a spatial-temporal perspective. To achieve this, the DBSCAN algorithm was employed to uncover spatial aggregation patterns among flight segments. Additionally, the K-Means algorithm was utilized to investigate the periodicity of flight block time. Based on our findings, it is observed that the majority of airport segments can be categorized into 4-5 distinct groups. Furthermore, it was discovered that taxi time exhibits a higher degree of periodicity compared to flight air time. Overall, these results provide valuable insights into the characteristics of flight operations, shedding light on the overlooked aspects of air traffic management.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Xiao Huang, Yong Tian, Kexin Niu, and Jiangchen Li "Analysis of flight operation characteristics based on DBSCAN and K-Means", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 1301856 (14 February 2024); https://doi.org/10.1117/12.3024759
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KEYWORDS
Transportation
Statistical analysis
Analytical research
Machine learning
Industry
Pattern recognition
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