  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.
