- Title
- A service network design for scheduled advanced air mobility using human-driven and autonomous air metro
- Creator
- Zhao, Runging; Koo, Tay T. R.; Liu, Wei; Lodewijks, Gabriel; Zhang, Fangni
- Relation
- Decision Analytics Journal Vol. 8, Issue September 2023, no. 100312
- Publisher Link
- http://dx.doi.org/10.1016/j.dajour.2023.100312
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2023
- Description
- Emerging modes of advanced air mobility are potential alternatives to current ground transport. This study proposes a service network design approach for the air metro, a pre-scheduled service with fixed routes that accommodate passengers for intra- or inter-city trips. The scenarios of human-driven and autonomous air metro are then compared, where the former has a labour cost for pilots and the latter has a higher capital costs such as vehicle and automation costs. Then, a rolling horizon optimisation approach is proposed, where the temporal length of a single rolling horizon is an early confirmation period plus a safety margin. The rolling horizon introduces decision and marginal arcs with different fleet, passenger, and pilot network capabilities. The optimised outputs on critical arcs are determined and fixed, while the marginal arcs can be continuously adjusted in the subsequent rolling horizons. Numerical studies are undertaken across all variables in the context of the Greater Metropolitan Area of Sydney, Australia. Results suggest that the human-driven air metro would be economically preferable until the utilisation cost of an autonomous aircraft can reduce by 60%. Furthermore, confirming the actual passenger demand at least 45 min in advance is recommended, and a single rolling horizon should be longer than 150 min.
- Subject
- advanced air mobility; air metro; autonomous vertical take-off and landing; rolling horizon optimisation; time-space network; SDG 11; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1491192
- Identifier
- uon:53040
- Identifier
- ISSN:2772-6622
- Rights
- © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
- Language
- eng
- Full Text
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