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Restricted Airspace Unit Identification Using Density-Based Spatial Clustering of Applications with Noise

Author

Listed:
  • Yong Tian

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Bojia Ye

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Lili Wan

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Minhao Yang

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Dawei Xing

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

This paper first calculates the departure delay and arrival delay of each flight by mining historical flight data. Then, a new method based on density clustering for identification and visualization of restricted airspace units that considers this activity is proposed. The main objective is to identify the restricted airspace units by calculating the average delay time according to the accumulative delay time of airspace units and the accumulative delay flight. Therefore, the density-based spatial clustering of applications with noise (DBSCAN) clustering method is utilized to match the latitude and longitude coordinates of each spatial domain unit with its delay time to construct a feature matrix, and then clustering analysis is conducted according to the time period. The method aims at identifying the most severe restricted units in each period. The reliability and applicability of the proposed method are validated through a real case study with flight information from Beijing Capital International Airport, Hongqiao International Airport, and Baiyun International Airport during a typical day. The investigation shows that the DBSCAN clustering method can identify the restricted spatial units intuitively on the six flight paths between Beijing Capital International Airport, Hongqiao International Airport, and Baiyun International Airport.

Suggested Citation

  • Yong Tian & Bojia Ye & Lili Wan & Minhao Yang & Dawei Xing, 2019. "Restricted Airspace Unit Identification Using Density-Based Spatial Clustering of Applications with Noise," Sustainability, MDPI, vol. 11(21), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:5962-:d:280515
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    References listed on IDEAS

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    1. Diana Michalek Pfeil & Hamsa Balakrishnan, 2012. "Identification of Robust Terminal-Area Routes in Convective Weather," Transportation Science, INFORMS, vol. 46(1), pages 56-73, February.
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    Cited by:

    1. Shiuan Wan & Yi-Ping Wang, 2020. "The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data," Agriculture, MDPI, vol. 10(10), pages 1-17, October.

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