Risk-aware Urban Air Mobility Network Design with Overflow Redundancy

TL;DR

This paper introduces a risk-aware design for urban air mobility networks that incorporates reserve capacity and redundancy to improve safety and efficiency during disruptions, using optimization models and case studies.

Contribution

It proposes a novel methodology for designing UAM networks with backup vertiports and corridors, optimizing for maximum throughput under various disruption scenarios.

Findings

Networks with reserve capacity outperform those without in throughput.

Optimization effectively identifies optimal backup vertiport locations.

Case studies demonstrate improved flexibility and resilience.

Abstract

Urban air mobility (UAM), as envisioned by aviation professionals, will transport passengers and cargo at low altitudes within urban and suburban areas. To operate in urban environments, precise air traffic management, in particular the management of traffic overflows due to physical and operational disruptions will be critical to ensuring system safety and efficiency. To this end, we propose UAM network design with reserve capacity, i.e., a design where alternative landing options and flight corridors are explicitly considered as a means of improving contingency management. Similar redundancy considerations are incorporated in the design of many critical infrastructures, yet remain unexploited in the air transportation literature. In our methodology, we first model how disruptions to a given UAM network might impact on the nominal traffic flow and how this flow might be re-accommodated on an extended network with reserve capacity. Then, through an optimization problem, we select the locations and capacities for the backup vertiports with the maximal expected throughput of the extended network over all possible disruption scenarios, while the throughput is the maximal amount of flights that the network can accommodate per unit of time. We show that we can obtain the solution for the corresponding bi-level and bi-linear optimization problem by solving a mixed-integer linear program. We demonstrate our methodology in the case study using networks from Milwaukee, Atlanta, and Dallas--Fort Worth metropolitan areas and show how the throughput and flexibility of the UAM networks with reserve capacity can outcompete those without.