RL-based Scheduling of an AAM Traffic Network

Arinc Tutku Altun; Yan Xu; Gokhan Inalhan; Michael W. Hardt

doi:10.1109/CAI54212.2023.00045

RL-based Scheduling of an AAM Traffic Network

Arinc Tutku Altun^*, Yan Xu, Gokhan Inalhan, Michael W. Hardt

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

This study presents an approach for pre-flight planning process to be used in the future Advanced Air Mobility (AAM) system especially after contingency situations and relevant activities take place. The methodology for scheduling is modeled as a reinforcement learning (RL) agent that resolves potential conflicts for the traffic and balances the demand and capacity at vertiports. The reason behind to use RL is that specific problem requires a very quick response since it also deals with resolving conflicts that are observed between the flights that are about to take-off and the contingent flights that diverted for an emergency landing. The main objective of this work is to develop a pre-flight planning service to work compatible with contingency management activities for enhancing the contingency management process for the AAM system.

Original language	English
Title of host publication	Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	87-88
Number of pages	2
ISBN (Electronic)	9798350339840
DOIs	https://doi.org/10.1109/CAI54212.2023.00045
Publication status	Published - 2023
Externally published	Yes
Event	2023 IEEE Conference on Artificial Intelligence, CAI 2023 - Santa Clara, United States Duration: 5 Jun 2023 → 6 Jun 2023

Publication series

Name	Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023

Conference

Conference	2023 IEEE Conference on Artificial Intelligence, CAI 2023
Country/Territory	United States
City	Santa Clara
Period	5/06/23 → 6/06/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

AAM
UTM
contingency management
demand capacity balancing
potential conflict resolution
pre-flight planning
reinforcement learning

Access to Document

10.1109/CAI54212.2023.00045

Cite this

@inproceedings{d44c24bc0b524e2595948990d103fb26,

title = "RL-based Scheduling of an AAM Traffic Network",

abstract = "This study presents an approach for pre-flight planning process to be used in the future Advanced Air Mobility (AAM) system especially after contingency situations and relevant activities take place. The methodology for scheduling is modeled as a reinforcement learning (RL) agent that resolves potential conflicts for the traffic and balances the demand and capacity at vertiports. The reason behind to use RL is that specific problem requires a very quick response since it also deals with resolving conflicts that are observed between the flights that are about to take-off and the contingent flights that diverted for an emergency landing. The main objective of this work is to develop a pre-flight planning service to work compatible with contingency management activities for enhancing the contingency management process for the AAM system.",

keywords = "AAM, UTM, contingency management, demand capacity balancing, potential conflict resolution, pre-flight planning, reinforcement learning",

author = "Altun, {Arinc Tutku} and Yan Xu and Gokhan Inalhan and Hardt, {Michael W.}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Conference on Artificial Intelligence, CAI 2023 ; Conference date: 05-06-2023 Through 06-06-2023",

year = "2023",

doi = "10.1109/CAI54212.2023.00045",

language = "English",

series = "Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "87--88",

booktitle = "Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023",

address = "United States",

}

Altun, AT, Xu, Y, Inalhan, G & Hardt, MW 2023, RL-based Scheduling of an AAM Traffic Network. in Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023. Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023, Institute of Electrical and Electronics Engineers Inc., pp. 87-88, 2023 IEEE Conference on Artificial Intelligence, CAI 2023, Santa Clara, United States, 5/06/23. https://doi.org/10.1109/CAI54212.2023.00045

RL-based Scheduling of an AAM Traffic Network. / Altun, Arinc Tutku; Xu, Yan; Inalhan, Gokhan et al.
Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 87-88 (Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - RL-based Scheduling of an AAM Traffic Network

AU - Altun, Arinc Tutku

AU - Xu, Yan

AU - Inalhan, Gokhan

AU - Hardt, Michael W.

PY - 2023

Y1 - 2023

N2 - This study presents an approach for pre-flight planning process to be used in the future Advanced Air Mobility (AAM) system especially after contingency situations and relevant activities take place. The methodology for scheduling is modeled as a reinforcement learning (RL) agent that resolves potential conflicts for the traffic and balances the demand and capacity at vertiports. The reason behind to use RL is that specific problem requires a very quick response since it also deals with resolving conflicts that are observed between the flights that are about to take-off and the contingent flights that diverted for an emergency landing. The main objective of this work is to develop a pre-flight planning service to work compatible with contingency management activities for enhancing the contingency management process for the AAM system.

AB - This study presents an approach for pre-flight planning process to be used in the future Advanced Air Mobility (AAM) system especially after contingency situations and relevant activities take place. The methodology for scheduling is modeled as a reinforcement learning (RL) agent that resolves potential conflicts for the traffic and balances the demand and capacity at vertiports. The reason behind to use RL is that specific problem requires a very quick response since it also deals with resolving conflicts that are observed between the flights that are about to take-off and the contingent flights that diverted for an emergency landing. The main objective of this work is to develop a pre-flight planning service to work compatible with contingency management activities for enhancing the contingency management process for the AAM system.

KW - AAM

KW - UTM

KW - contingency management

KW - demand capacity balancing

KW - potential conflict resolution

KW - pre-flight planning

KW - reinforcement learning

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DO - 10.1109/CAI54212.2023.00045

M3 - Conference contribution

AN - SCOPUS:85168680602

T3 - Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023

SP - 87

EP - 88

BT - Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Conference on Artificial Intelligence, CAI 2023

Y2 - 5 June 2023 through 6 June 2023

ER -

RL-based Scheduling of an AAM Traffic Network

Abstract

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Keywords

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