@inproceedings{e78326ccdad24700b4de27e72497e9d3,
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 = "英语",
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 = "美国",
}