Clusters and communities in air traffic delay networks
Name
Clusters and Communities in Air.pdf
Size
1.94 MB
Format
Adobe PDF
Checksum (MD5)
742a2c56ba5c7b3dacb9857d34730f2e
Author(s)
Kavassery Gopalakrishnan, Karthik
Balakrishnan, Hamsa
Jordan, Richard K.
Date Issued
August 2016
Journal
2016 American Control Conference (ACC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Gopalakrishnan, Karthik, Hamsa Balakrishnan, and Richard Jordan. “Clusters and Communities in Air Traffic Delay Networks.” 2016 American Control Conference (ACC), July 6-8 2016, Boston, Massachusetts, Institute of Electrical and Electronics Engineers (IEEE), July 2016).
Version
Author's final manuscript
Abstract
The air transportation system is a network of many interacting, capacity-constrained elements. When the demand for airport and airspace resources exceed the available capacities of these resources, delays occur. The state of the air transportation system at any time can be represented as a weighted directed graph in which the nodes correspond to airports, and the weight on each arc is the delay experienced by departures on that origin-destination pair. Over the course of any day, the state of the system progresses through a time-series, where the state at any time-step is the weighted directed graph described above. This paper presents algorithms for the clustering of air traffic delay network data from the US National Airspace System, in order to identify characteristic delay states (i.e., weighted directed graphs) as well as characteristic types-of-days (i.e., sequences of such weighted directed graphs) that are experienced by the air transportation system. The similarity of delay states during clustering are evaluated on the basis of not only the in- and out-degrees of the nodes (the total inbound and outbound delays), but also network-theoretic properties such as the eigenvector centralities, and the hub and authority scores of different nodes. Finally, the paper looks at community detection, that is, the grouping of nodes (airports) based on their similarities within a system delay state. The type of day is found to have an impact on the observed community structures.
MIT Department
Lincoln Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ACC.2016.7525502
