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

TL;DR

This paper introduces a graph reinforcement learning approach for real-time vertiport scheduling in Urban Air Mobility, improving safety, efficiency, and battery use through simulation-based evaluation.

Contribution

It presents a novel graph-based RL method for vertiport scheduling in UAM, integrating GCNs for feature extraction and decision policies, outperforming baseline methods.

Findings

Graph RL outperforms basic RL and random baselines.

The approach reduces delays and collisions.

Battery consumption is optimized.

Abstract

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.