Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs
Name
SSRN-id3734008.pdf
Description
Submitted version
Size
2.49 MB
Format
Adobe PDF
Checksum (MD5)
74f4433b25de85c482da00eb1a3d1183
Author(s)
Jacquillat, Alexandre
Date Issued
2022
Journal
Transportation Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Jacquillat, Alexandre. 2022. "Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs." Transportation Science, 56 (2).
Version
Original manuscript
Abstract
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.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/TRSC.2021.1081
