Optimizing Urban Air Mobility Operations in a Corridor Network

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• McDonald, Spencer T.

Advisor dc:contributor.advisor

• Jacquillat, Alexandre

Statement dc:rights

• CC0 - Public Domain
• Copyright Public Domain

Licence dc:rights.uri

• https://creativecommons.org/publicdomain/zero/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151838

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151838