Graph Learning-based Fleet Scheduling for Urban Air Mobility under
  Operational Constraints, Varying Demand & Uncertainties
Steve Paul Jhoel Witter Souma Chowdhury
This paper develops a graph reinforcement learning approach to online
planning of the schedule and destinations of electric aircraft that comprise an
urban air mobility (UAM) fleet operating across multiple vertiports. This fleet
scheduling problem is formulated to consider time-varying demand, constraints
related to vertiport capacity, aircraft capacity and airspace safety
guidelines, uncertainties related to take-off delay, weather-induced route
closures, and unanticipated aircraft downtime. Collectively, such a formulation
presents greater complexity, and potentially increased realism, than in
existing UAM fleet planning implementations. To address these complexities, a
new policy architecture is constructed, primary components of which include:
graph capsule conv-nets for encoding vertiport and aircraft-fleet states both
abstracted as graphs; transformer layers encoding time series information on
demand and passenger fare; and a Multi-head Attention-based decoder that uses
the encoded information to compute the probability of selecting each available
destination for an aircraft. Trained with Proximal Policy Optimization, this
policy architecture shows significantly better performance in terms of daily
averaged profits on unseen test scenarios involving 8 vertiports and 40
aircraft, when compared to a random baseline and genetic algorithm-derived
optimal solutions, while being nearly 1000 times faster in execution than the
latter.