Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152779
Title: Real-time departure slotting in mixed-mode operations using deep reinforcement learning : a case study of Zurich airport
Authors: Pham, Duc-Thinh
Chan, Li Long
Alam, Sameer 
Koelle, Rainer
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Aeronautical engineering::Aviation
Issue Date: 2021
Source: Pham, D., Chan, L. L., Alam, S. & Koelle, R. (2021). Real-time departure slotting in mixed-mode operations using deep reinforcement learning : a case study of Zurich airport. Fourteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2021), 62-.
Abstract: A mixed-mode runway operation increases the runway capacity by allowing simultaneous arrival and departure operations on the same runway. However, this requires careful evaluation of safe separation by experienced Air Traffic Controllers (ATCOs). In daily operation, ATCOs need to make real-time decisions for departure slotting. However, an increase in runway capacity is not always guaranteed due to the stochastic nature of arrivals and departures and associated environmental parameters. To support ATCOs in making real-time departure slotting decisions, this paper proposes a Deep Reinforcement Learning approach to suggest departure slots within an incoming stream of arrivals while considering operational constraints and uncertainties. In this work, novel state representation and reward mechanism are designed to facilitate the learning process. Experimentation on A-SMGCS data from Zurich airport shows that the proposed approach achieves an efficiency ratio of more than 83.8% of the expected runway capacity while maintaining safe separation distances in mixed-mode operations. The results of this work have demonstrated the potentials of Deep Reinforcement Learning in solving decision-making problems in Air Traffic Management.
URI: https://hdl.handle.net/10356/152779
Rights: © 2021 The Author(s). All rights reserved. This paper was published by ATM Seminar in Proceedings of Fourteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2021) and is made available with permission of The Author(s).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

Files in This Item:
File Description SizeFormat 
ATM_Seminar_2021_Departure_Slotting (3).pdf3.4 MBAdobe PDFThumbnail
View/Open

Page view(s)

58
Updated on Jan 20, 2022

Download(s) 50

64
Updated on Jan 20, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.