Optimization-Guided Exploration of Advanced Air Mobility Congestion Management Strategies with Stochastic Demands

Abstract

Advanced Air Mobility (AAM) represents an evolution of the air transportation system by introducing low-altitude, potentially high-traffic environments. AAM operations will be enabled by both new aircraft, as well as new safety- and efficiency-critical supporting infrastructure. Published concepts of operations from both public and private sector entities establish notions such as federated management of the airspace and public-private partnerships for AAM air traffic, but there is a gap in the literature in terms of integrated tools that consider all three critical elements: AAM fleet operators (\emph{lower} layer), airspace service providers (\emph{middle} layer), and overall system governance from the legacy air navigation service provider (\emph{upper} layer). In this work, we explore modeling congestion management within the AAM setting using a bi-level optimization approach, focusing on (1) time-varying, stochastic AAM demand, (2) differing congestion management strategies, and (3) the impact of unscheduled, \enquote{pop-up} demand. We show that our bi-level formulation can be tractably solved using a Neural Network-based surrogate which returns solution qualities within 0.1-5.2\% of the optimal solution. Additionally, we show that our congestion management strategies can reduce congestion by 25.7-39.8\% when compared to the scenario of no strategies being applied. Finally, we also show that while pop-up demand degrades congestion conditions, our congestion management strategies fare better against pop-up demand than the no strategy scenario. The work herein contributes a rigorous modeling and simulation tool to help evaluate future AAM traffic management concepts and strategies.