Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs
<jats:p> Ground delay programs (GDPs) comprise the main interventions to optimize flight operations in congested air traffic networks. The core GDP objective is to minimize flight delays, but this may not result in optimal outcomes for passengers—especially with connecting itineraries. This pa...
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| Formáid: | Alt |
| Teanga: | English |
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Institute for Operations Research and the Management Sciences (INFORMS)
2022
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| Rochtain ar líne: | https://hdl.handle.net/1721.1/144176 |
| _version_ | 1826215876218585088 |
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| author | Jacquillat, Alexandre |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Jacquillat, Alexandre |
| author_sort | Jacquillat, Alexandre |
| collection | MIT |
| description | <jats:p> Ground delay programs (GDPs) comprise the main interventions to optimize flight operations in congested air traffic networks. The core GDP objective is to minimize flight delays, but this may not result in optimal outcomes for passengers—especially with connecting itineraries. This paper proposes a novel passenger-centric optimization approach to GDPs by balancing flight and passenger delays in large-scale networks. For tractability, we decompose the problem using a rolling procedure, enabling the model’s implementation in manageable runtimes. Computational results based on real-world data suggest that our modeling and computational framework can reduce passenger delays significantly at small increases in flight delay costs through two main mechanisms: (i) delay allocation (delaying versus prioritizing flights) and (ii) delay introduction (holding flights to avoid passenger misconnections). In practice, however, passenger itineraries are unknown to air traffic managers; accordingly, we propose statistical learning models to predict passenger itineraries and optimize GDP operations accordingly. Results show that the proposed passenger-centric approach is highly robust to imperfect knowledge of passenger itineraries and can provide significant benefits even in the current decentralized environment based on collaborative decision making. </jats:p> |
| first_indexed | 2024-09-23T16:38:26Z |
| format | Article |
| id | mit-1721.1/144176 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:38:26Z |
| publishDate | 2022 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1441762023-12-08T19:48:03Z Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs Jacquillat, Alexandre Sloan School of Management <jats:p> Ground delay programs (GDPs) comprise the main interventions to optimize flight operations in congested air traffic networks. The core GDP objective is to minimize flight delays, but this may not result in optimal outcomes for passengers—especially with connecting itineraries. This paper proposes a novel passenger-centric optimization approach to GDPs by balancing flight and passenger delays in large-scale networks. For tractability, we decompose the problem using a rolling procedure, enabling the model’s implementation in manageable runtimes. Computational results based on real-world data suggest that our modeling and computational framework can reduce passenger delays significantly at small increases in flight delay costs through two main mechanisms: (i) delay allocation (delaying versus prioritizing flights) and (ii) delay introduction (holding flights to avoid passenger misconnections). In practice, however, passenger itineraries are unknown to air traffic managers; accordingly, we propose statistical learning models to predict passenger itineraries and optimize GDP operations accordingly. Results show that the proposed passenger-centric approach is highly robust to imperfect knowledge of passenger itineraries and can provide significant benefits even in the current decentralized environment based on collaborative decision making. </jats:p> 2022-08-01T16:31:27Z 2022-08-01T16:31:27Z 2022 2022-08-01T16:20:15Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/144176 Jacquillat, Alexandre. 2022. "Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs." Transportation Science, 56 (2). en 10.1287/TRSC.2021.1081 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) SSRN |
| spellingShingle | Jacquillat, Alexandre Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs |
| title | Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs |
| title_full | Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs |
| title_fullStr | Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs |
| title_full_unstemmed | Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs |
| title_short | Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs |
| title_sort | predictive and prescriptive analytics toward passenger centric ground delay programs |
| url | https://hdl.handle.net/1721.1/144176 |
| work_keys_str_mv | AT jacquillatalexandre predictiveandprescriptiveanalyticstowardpassengercentricgrounddelayprograms |