Graph Signal Processing Techniques for Analyzing Aviation Disruptions
<jats:p> Understanding the characteristics of air-traffic delays and disruptions is critical for developing ways to mitigate their significant economic and environmental impacts. Conventional delay-performance metrics reflect only the magnitude of incurred flight delays at airports; in this wo...
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| Médium: | Článek |
| Jazyk: | English |
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Institute for Operations Research and the Management Sciences (INFORMS)
2022
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| On-line přístup: | https://hdl.handle.net/1721.1/145271 |
| _version_ | 1826211929076531200 |
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| author | Li, Max Z Gopalakrishnan, Karthik Pantoja, Kristyn Balakrishnan, Hamsa |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Li, Max Z Gopalakrishnan, Karthik Pantoja, Kristyn Balakrishnan, Hamsa |
| author_sort | Li, Max Z |
| collection | MIT |
| description | <jats:p> Understanding the characteristics of air-traffic delays and disruptions is critical for developing ways to mitigate their significant economic and environmental impacts. Conventional delay-performance metrics reflect only the magnitude of incurred flight delays at airports; in this work, we show that it is also important to characterize the spatial distribution of delays across a network of airports. We analyze graph-supported signals, leveraging techniques from spectral theory and graph-signal processing to compute analytical and simulation-driven bounds for identifying outliers in spatial distribution. We then apply these methods to the case of airport-delay networks and demonstrate the applicability of our methods by analyzing U.S. airport delays from 2008 through 2017. We also perform an airline-specific analysis, deriving insights into the delay dynamics of individual airline subnetworks. Through our analysis, we highlight key differences in delay dynamics between different types of disruptions, ranging from nor’easters and hurricanes to airport outages. We also examine delay interactions between airline subnetworks and the system-wide network and compile an inventory of outlier days that could guide future aviation operations and research. In doing so, we demonstrate how our approach can provide operational insights in an air-transportation setting. Our analysis provides a complementary metric to conventional aviation-delay benchmarks and aids airlines, traffic-flow managers, and transportation-system planners in quantifying off-nominal system performance. </jats:p> |
| first_indexed | 2024-09-23T15:13:38Z |
| format | Article |
| id | mit-1721.1/145271 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:13:38Z |
| publishDate | 2022 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1452712023-12-08T17:48:25Z Graph Signal Processing Techniques for Analyzing Aviation Disruptions Li, Max Z Gopalakrishnan, Karthik Pantoja, Kristyn Balakrishnan, Hamsa Massachusetts Institute of Technology. Department of Aeronautics and Astronautics <jats:p> Understanding the characteristics of air-traffic delays and disruptions is critical for developing ways to mitigate their significant economic and environmental impacts. Conventional delay-performance metrics reflect only the magnitude of incurred flight delays at airports; in this work, we show that it is also important to characterize the spatial distribution of delays across a network of airports. We analyze graph-supported signals, leveraging techniques from spectral theory and graph-signal processing to compute analytical and simulation-driven bounds for identifying outliers in spatial distribution. We then apply these methods to the case of airport-delay networks and demonstrate the applicability of our methods by analyzing U.S. airport delays from 2008 through 2017. We also perform an airline-specific analysis, deriving insights into the delay dynamics of individual airline subnetworks. Through our analysis, we highlight key differences in delay dynamics between different types of disruptions, ranging from nor’easters and hurricanes to airport outages. We also examine delay interactions between airline subnetworks and the system-wide network and compile an inventory of outlier days that could guide future aviation operations and research. In doing so, we demonstrate how our approach can provide operational insights in an air-transportation setting. Our analysis provides a complementary metric to conventional aviation-delay benchmarks and aids airlines, traffic-flow managers, and transportation-system planners in quantifying off-nominal system performance. </jats:p> 2022-09-06T17:15:27Z 2022-09-06T17:15:27Z 2021 2022-09-06T17:03:45Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/145271 Li, Max Z, Gopalakrishnan, Karthik, Pantoja, Kristyn and Balakrishnan, Hamsa. 2021. "Graph Signal Processing Techniques for Analyzing Aviation Disruptions." Transportation Science, 55 (3). en 10.1287/TRSC.2020.1026 Transportation Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) MIT web domain |
| spellingShingle | Li, Max Z Gopalakrishnan, Karthik Pantoja, Kristyn Balakrishnan, Hamsa Graph Signal Processing Techniques for Analyzing Aviation Disruptions |
| title | Graph Signal Processing Techniques for Analyzing Aviation Disruptions |
| title_full | Graph Signal Processing Techniques for Analyzing Aviation Disruptions |
| title_fullStr | Graph Signal Processing Techniques for Analyzing Aviation Disruptions |
| title_full_unstemmed | Graph Signal Processing Techniques for Analyzing Aviation Disruptions |
| title_short | Graph Signal Processing Techniques for Analyzing Aviation Disruptions |
| title_sort | graph signal processing techniques for analyzing aviation disruptions |
| url | https://hdl.handle.net/1721.1/145271 |
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