Optimization-Guided Exploration of Advanced Air Mobility Congestion Management Strategies with Stochastic Demands
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914052023779328 |
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| author | Wu, Haochen Weitz, Lesley A. Henderson, Jeffrey M. Li, Max Z. |
| author_facet | Wu, Haochen Weitz, Lesley A. Henderson, Jeffrey M. Li, Max Z. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18505 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimization-Guided Exploration of Advanced Air Mobility Congestion Management Strategies with Stochastic Demands Wu, Haochen Weitz, Lesley A. Henderson, Jeffrey M. Li, Max Z. Optimization and Control 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. |
| title | Optimization-Guided Exploration of Advanced Air Mobility Congestion Management Strategies with Stochastic Demands |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2509.18505 |