Clusters and communities in air traffic delay networks
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...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/108052 https://orcid.org/0000-0003-3195-7828 https://orcid.org/0000-0002-8624-7041 |
| _version_ | 1811083341526466560 |
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| author | Kavassery Gopalakrishnan, Karthik Balakrishnan, Hamsa Jordan, Richard K. |
| author2 | Lincoln Laboratory |
| author_facet | Lincoln Laboratory Kavassery Gopalakrishnan, Karthik Balakrishnan, Hamsa Jordan, Richard K. |
| author_sort | Kavassery Gopalakrishnan, Karthik |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:31:32Z |
| format | Article |
| id | mit-1721.1/108052 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:31:32Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1080522022-09-28T08:18:23Z Clusters and communities in air traffic delay networks Kavassery Gopalakrishnan, Karthik Balakrishnan, Hamsa Jordan, Richard K. Lincoln Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Kavassery Gopalakrishnan, Karthik Balakrishnan, Hamsa Jordan, Richard K. 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. United States. National Aeronautics and Space Administration (FA8721-05-C-0002) National Science Foundation (U.S.) (1239054) 2017-04-11T17:54:30Z 2017-04-11T17:54:30Z 2016-08 2016-07 Article http://purl.org/eprint/type/ConferencePaper 978-1-4673-8682-1 2378-5861 http://hdl.handle.net/1721.1/108052 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). https://orcid.org/0000-0003-3195-7828 https://orcid.org/0000-0002-8624-7041 en_US http://dx.doi.org/10.1109/ACC.2016.7525502 2016 American Control Conference (ACC) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Kavassery Gopalakrishnan, Karthik Balakrishnan, Hamsa Jordan, Richard K. Clusters and communities in air traffic delay networks |
| title | Clusters and communities in air traffic delay networks |
| title_full | Clusters and communities in air traffic delay networks |
| title_fullStr | Clusters and communities in air traffic delay networks |
| title_full_unstemmed | Clusters and communities in air traffic delay networks |
| title_short | Clusters and communities in air traffic delay networks |
| title_sort | clusters and communities in air traffic delay networks |
| url | http://hdl.handle.net/1721.1/108052 https://orcid.org/0000-0003-3195-7828 https://orcid.org/0000-0002-8624-7041 |
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