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

Yong Tian, Bojia Ye, Lili Wan, Minhao Yang and Dawei Xing
Additional contact information
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

Sustainability, 2019, vol. 11, issue 21, 1-15

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.

Keywords: airspace; flight delay; machine learning; clustering algorithm; visualization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/21/5962/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/21/5962/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:21:p:5962-:d:280515

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().