Slot allocation for a multiple-airport system considering airspace capacity and flying time uncertainty

Prior research on slot allocation has focused on a single airport, with little attention paid to the multiple-airport systems (MAS) that consist of at least two major airports. Scheduled flights at different airports may have conflicts regarding shared fixes (i.e., route points) or routes, thus caus...

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Main Authors: WANG, Yanjun, LIU, Chang, WANG, Hai, DUONG, Vu
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Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/sis_research/8247
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-92502023-10-26T01:36:06Z Slot allocation for a multiple-airport system considering airspace capacity and flying time uncertainty WANG, Yanjun LIU, Chang WANG, Hai DUONG, Vu Prior research on slot allocation has focused on a single airport, with little attention paid to the multiple-airport systems (MAS) that consist of at least two major airports. Scheduled flights at different airports may have conflicts regarding shared fixes (i.e., route points) or routes, thus causing airspace congestion and flight delays. Traffic demand at a critical fix depends on both the departure/arrival time of the flights and the flying times between the airport and the fix, whereas flying times exhibit a stochastic nature due to various factors such as air traffic control strategies, aircraft performance, and weather. In this paper, we develop a chance-constrained slot allocation model for an MAS that optimizes slot allocation for multiple airports while considering fix capacity constraints. To capture the uncertainty of flying times, stochastic chance constraints are formulated and a scenario generation method is proposed to solve the model. We apply our model to allocate slots in the MAS of Guangdong-Hong Kong-Macao Greater Bay area. The results show that the schedules generated by the proposed model outperform those from the certainty model and the original schedules. Traffic flow at critical fixes is more robust to various operating scenarios with the cost of a small number of increased slot displacements. Our findings highlight the importance of flying time uncertainty in allocating slot and airspace capacity, and the proposed model provides a useful tool for slot coordinators seeking to effectively manage airport slots in an MAS. 2023-08-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/8247 info:doi/10.1016/j.trc.2023.104185 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University slot allocation multiple-airport system uncertainty model chance constraint scenario generation Artificial Intelligence and Robotics Transportation
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic slot allocation
multiple-airport system
uncertainty model
chance constraint
scenario generation
Artificial Intelligence and Robotics
Transportation
spellingShingle slot allocation
multiple-airport system
uncertainty model
chance constraint
scenario generation
Artificial Intelligence and Robotics
Transportation
WANG, Yanjun
LIU, Chang
WANG, Hai
DUONG, Vu
Slot allocation for a multiple-airport system considering airspace capacity and flying time uncertainty
description Prior research on slot allocation has focused on a single airport, with little attention paid to the multiple-airport systems (MAS) that consist of at least two major airports. Scheduled flights at different airports may have conflicts regarding shared fixes (i.e., route points) or routes, thus causing airspace congestion and flight delays. Traffic demand at a critical fix depends on both the departure/arrival time of the flights and the flying times between the airport and the fix, whereas flying times exhibit a stochastic nature due to various factors such as air traffic control strategies, aircraft performance, and weather. In this paper, we develop a chance-constrained slot allocation model for an MAS that optimizes slot allocation for multiple airports while considering fix capacity constraints. To capture the uncertainty of flying times, stochastic chance constraints are formulated and a scenario generation method is proposed to solve the model. We apply our model to allocate slots in the MAS of Guangdong-Hong Kong-Macao Greater Bay area. The results show that the schedules generated by the proposed model outperform those from the certainty model and the original schedules. Traffic flow at critical fixes is more robust to various operating scenarios with the cost of a small number of increased slot displacements. Our findings highlight the importance of flying time uncertainty in allocating slot and airspace capacity, and the proposed model provides a useful tool for slot coordinators seeking to effectively manage airport slots in an MAS.
format text
author WANG, Yanjun
LIU, Chang
WANG, Hai
DUONG, Vu
author_facet WANG, Yanjun
LIU, Chang
WANG, Hai
DUONG, Vu
author_sort WANG, Yanjun
title Slot allocation for a multiple-airport system considering airspace capacity and flying time uncertainty
title_short Slot allocation for a multiple-airport system considering airspace capacity and flying time uncertainty
title_full Slot allocation for a multiple-airport system considering airspace capacity and flying time uncertainty
title_fullStr Slot allocation for a multiple-airport system considering airspace capacity and flying time uncertainty
title_full_unstemmed Slot allocation for a multiple-airport system considering airspace capacity and flying time uncertainty
title_sort slot allocation for a multiple-airport system considering airspace capacity and flying time uncertainty
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/sis_research/8247
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