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oai:dspace.mit.edu:1721.1/1518382023-08-24T03:36:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Jacquillat, Alexandre McDonald, Spencer T. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Civil and Environmental Engineering 2023-08-23T16:12:37Z 2023-08-23T16:12:37Z 2023-06 2023-06-22T14:49:49.923Z https://hdl.handle.net/1721.1/151838 https://orcid.org/ 0009-0004-6976-4698 Electric vertical-takeoff-and-landing vehicles give rise to Urban Air Mobility (UAM) concepts. Initial UAM systems are projected to operate high volumes of flights within a capacitated corridor network. This paper develops a tractable methodology to optimize vehicle dispatching and routing in UAM networks, along with flight trajectories between origin and destination, as well as flow directionality in corridors. We formulate an integer optimization model in a time-space-network that exploits a subpath structure at the flight level. We propose a column generation algorithm to decompose vehicle dispatching decisions in a master problem and flight trajectories in a pricing problem, using a tailored backward label-setting algorithm. This methodology scales to practical instances, with up to 50 vertiports, hundreds of corridor conjunctions, and 1,000 trip requests. We develop a data-driven experimental setup capturing real-world travel demand, air traffic infrastructure and weather patterns. Results demonstrate the benefits of the comprehensive optimization approach developed in this paper, as compared to benchmarks that do not capture flow directionality or operating capacities. This methodology identifies the bottlenecks created by legacy corridors, horizontal separation requirements and adverse weather to inform the design of emerging UAM systems. S.M. S.M. Massachusetts Institute of Technology CC0 - Public Domain Copyright Public Domain https://creativecommons.org/publicdomain/zero/1.0/ Optimizing Urban Air Mobility Operations in a Corridor Network Thesis application/pdf Master Master of Science in Transportation Master of Science in Aeronautics and Astronautics Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="5d18f1eb-74c2-40cc-b307-e315471b0ef0"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Optimizing Urban Air Mobility Operations in a Corridor Network</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2023-06</PublicationDate> <Authors> <Author> <DisplayName>McDonald, Spencer T.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://creativecommons.org/publicdomain/zero/1.0/</License> <Abstract>Electric vertical-takeoff-and-landing vehicles give rise to Urban Air Mobility (UAM) concepts. Initial UAM systems are projected to operate high volumes of flights within a capacitated corridor network. This paper develops a tractable methodology to optimize vehicle dispatching and routing in UAM networks, along with flight trajectories between origin and destination, as well as flow directionality in corridors. We formulate an integer optimization model in a time-space-network that exploits a subpath structure at the flight level. We propose a column generation algorithm to decompose vehicle dispatching decisions in a master problem and flight trajectories in a pricing problem, using a tailored backward label-setting algorithm. This methodology scales to practical instances, with up to 50 vertiports, hundreds of corridor conjunctions, and 1,000 trip requests. We develop a data-driven experimental setup capturing real-world travel demand, air traffic infrastructure and weather patterns. Results demonstrate the benefits of the comprehensive optimization approach developed in this paper, as compared to benchmarks that do not capture flow directionality or operating capacities. This methodology identifies the bottlenecks created by legacy corridors, horizontal separation requirements and adverse weather to inform the design of emerging UAM systems.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>