Integrated Conflict Management for UAM with Strategic Demand Capacity Balancing and Learning-based Tactical Deconfliction

TL;DR

This paper presents a novel integrated framework combining demand capacity balancing and reinforcement learning to improve safety and efficiency in urban air mobility conflict management.

Contribution

It introduces a combined strategic and tactical conflict management approach using DCB and reinforcement learning, enhancing safety and operational efficiency in UAM.

Findings

Reinforcement learning performance improves with DCB preconditioning.

The integrated approach meets safety targets unattainable by other methods.

Operational efficiency surpasses alternative conflict management solutions.

Abstract

Urban air mobility (UAM) has the potential to revolutionize our daily transportation, offering rapid and efficient deliveries of passengers and cargo between dedicated locations within and around the urban environment. Before the commercialization and adoption of this emerging transportation mode, however, aviation safety must be guaranteed, i.e., all the aircraft have to be safely separated by strategic and tactical deconfliction. Reinforcement learning has demonstrated effectiveness in the tactical deconfliction of en route commercial air traffic in simulation. However, its performance is found to be dependent on the traffic density. In this project, we propose a novel framework that combines demand capacity balancing (DCB) for strategic conflict management and reinforcement learning for tactical separation. By using DCB to precondition traffic to proper density levels, we show that reinforcement learning can achieve much better performance for tactical safety separation. Our results also indicate that this DCB preconditioning can allow target levels of safety to be met that are otherwise impossible. In addition, combining strategic DCB with reinforcement learning for tactical separation can meet these safety levels while achieving greater operational efficiency than alternative solutions.