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

arXiv: https://arxiv.org/abs/2305.10556 · HTML: https://mlsanity.com/paper/2305.10556/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [A Survey on Reinforcement Learning in Aviation Applications](https://mlsanity.com/paper/2211.02147/) (2022)
- [Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance](https://mlsanity.com/paper/2105.02331/) (2021)
- [Collision Risk and Operational Impact of Speed Change Advisories as Aircraft Collision Avoidance Maneuvers](https://mlsanity.com/paper/2204.14250/) (2022)

## Built on by (most cited first, 6 of 6)

- [Robust Multi-Agent Reinforcement Learning for Small UAS Separation Assurance under GPS Degradation and Spoofing](https://mlsanity.com/paper/2603.28900/) (2026)
- [Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework](https://mlsanity.com/paper/2508.16440/) (2025)
- [eVTOL Aircraft Energy Overhead Estimation under Conflict Resolution in High-Density Airspaces](https://mlsanity.com/paper/2604.06093/) (2026)
- [Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.01041/) (2026)
- [Runtime Safety Filtering for Learned Small UAS Separation Policies under GNSS Degradation](https://mlsanity.com/paper/2607.10014/) (2026)
- [Declarative Problem Solving in UAM Strategic Deconfliction](https://mlsanity.com/paper/2607.21197/) (2026)

## Abstract (arXiv)

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.