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Protocol-Based Congestion Management for Advanced Air Mobility

Authors: Christopher Chin; Karthik Gopalakrishnan; Hamsa Balakrishnan; Maxim Egorov; Antony Evans;

Protocol-Based Congestion Management for Advanced Air Mobility

Abstract

Advanced air mobility operations are expected to significantly increase the demand for limited airspace resources. Two key features distinguish advanced air mobility operations from commercial aviation. First, unlike commercial aviation, where flight schedules are set months in advance, advanced air mobility demand is dynamic (i.e., flights are planned with a much shorter lead-time). Thus, operators benefit from planning in shorter time horizons and can confidently share their desired flight trajectories only for the near future. In addition, operators may be unwilling to share estimates of the full trajectory, for competitive reasons. The second key feature is the large-scale of operations. Thus, a centralized optimization approach may not scale to meet the expected levels of demand, and it offers no redundancy against communication failures. In this paper, we address these challenges by designing a protocol that determines the “rules-of-the-road” for airspace access. Our protocol centers on the construction of priority queues to determine access to each congested volume of airspace. We leverage the concepts of backpressure (measure of queue buildup) and cycle detection (vehicles that block each other from proceeding) to promote efficiency, and present several flight- and operator-level prioritization schemes. In the absence of actual demand data, we study three scenarios: random origin–destination missions, crossflow traffic patterns, and simulated hub-based package delivery operations. We evaluate our protocols on two performance measures: efficiency (i.e., magnitudes of delays) and fairness (i.e., equitable distribution of delay across flights and operators).

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Top 10%
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