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Passenger-centric Urban Air Mobility: Fairness trade-offs and operational efficiency

Urban Air Mobility (UAM) envisions safe transportation systems that will exploit new vertical takeoff and landing electric aircraft to transport passengers or cargo within urban and suburban areas. To be successful, it will require an integrated approach able to balance efficiency and safety while l...

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Bibliographic Details
Published in:Transportation research. Part C, Emerging technologies Emerging technologies, 2022-03, Vol.136, p.103519, Article 103519
Main Authors: Bennaceur, Mehdi, Delmas, Rémi, Hamadi, Youssef
Format: Article
Language:English
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Summary:Urban Air Mobility (UAM) envisions safe transportation systems that will exploit new vertical takeoff and landing electric aircraft to transport passengers or cargo within urban and suburban areas. To be successful, it will require an integrated approach able to balance efficiency and safety while leveraging common resources and information. In this work we focus on future urban air-taxi services, and present the first methods and algorithms to efficiently operate air-taxi at scale. Our approach is twofold. First, we use a passenger-centric perspective which introduces traveling classes, and information sharing between transport modes to differentiate quality of services. This helps smooth multimodal journeys and increase passenger satisfaction. Second, we provide a flight routing and recharging solution which minimizes direct operational costs while preserving long term battery life through reduced energy-intense recharging. Our methods, which surpass the performance of a general state-of-the-art commercial solver, are also used to gain meaningful insights on the design space of the air-taxi problem, including solutions to hidden fairness issues. [Display omitted] •First Urban Air Mobility model including brokers and operators.•QoS effects of introducing passenger classes are measured and explained.•Intra-class fairness issues are characterized.•Fairness control and resolution mechanisms are proposed.•Scalable online optimization algorithms are given.
ISSN:0968-090X
1879-2359
DOI:10.1016/j.trc.2021.103519