# Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning

arXiv: https://arxiv.org/abs/2308.09075 · HTML: https://mlsanity.com/paper/2308.09075/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search](https://mlsanity.com/paper/1810.10659/) (2018)
- [Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks](https://mlsanity.com/paper/1706.07450/) (2017)
- [Learning Scalable Policies over Graphs for Multi-Robot Task Allocation using Capsule Attention Networks](https://mlsanity.com/paper/2205.03321/) (2022)
- [Efficient Planning of Multi-Robot Collective Transport using Graph Reinforcement Learning with Higher Order Topological Abstraction](https://mlsanity.com/paper/2303.08933/) (2023)
- [Learning Robot Swarm Tactics over Complex Adversarial Environments](https://mlsanity.com/paper/2109.05663/) (2021)
- [Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System](https://mlsanity.com/paper/2201.04180/) (2022)
- [Graph Learning Based Decision Support for Multi-Aircraft Take-Off and Landing at Urban Air Mobility Vertiports](https://mlsanity.com/paper/2302.05849/) (2023)
- [Path Planning and Energy Management of Hybrid Air Vehicles for Urban Air Mobility](https://mlsanity.com/paper/2205.14464/) (2022)

## Built on by (most cited first, 3 of 3)

- [Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties](https://mlsanity.com/paper/2401.04851/) (2024)
- [Bigraph Matching Weighted with Learnt Incentive Function for Multi-Robot Task Allocation](https://mlsanity.com/paper/2403.07131/) (2024)
- [Learning-aided Bigraph Matching Approach to Multi-Crew Restoration of Damaged Power Networks Coupled with Road Transportation Networks](https://mlsanity.com/paper/2506.19703/) (2025)

## Abstract (arXiv)

Urban Air Mobility (UAM) promises a new dimension to decongested, safe, and fast travel in urban and suburban hubs. These UAM aircraft are conceived to operate from small airports called vertiports each comprising multiple take-off/landing and battery-recharging spots. Since they might be situated in dense urban areas and need to handle many aircraft landings and take-offs each hour, managing this schedule in real-time becomes challenging for a traditional air-traffic controller but instead calls for an automated solution. This paper provides a novel approach to this problem of Urban Air Mobility - Vertiport Schedule Management (UAM-VSM), which leverages graph reinforcement learning to generate decision-support policies. Here the designated physical spots within the vertiport's airspace and the vehicles being managed are represented as two separate graphs, with feature extraction performed through a graph convolutional network (GCN). Extracted features are passed onto perceptron layers to decide actions such as continue to hover or cruise, continue idling or take-off, or land on an allocated vertiport spot. Performance is measured based on delays, safety (no. of collisions) and battery consumption. Through realistic simulations in AirSim applied to scaled down multi-rotor vehicles, our results demonstrate the suitability of using graph reinforcement learning to solve the UAM-VSM problem and its superiority to basic reinforcement learning (with graph embeddings) or random choice baselines.