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Missed connections at transit airports are a source of both poor customer experience and reduced airline operational efficiency. Airlines typically handle missed connections by rebooking customers. Recently, airlines have started holding departing flights for some time in a rule-based manner to avoid missed connections. However, rule-based heuristics typically use information local to a flight and do not learn in a globally informed way across the entire network. We complement existing approaches by learning a policy for holding a flight to avoid misconnections, using reinforcement learning (RL). The state presented to the RL agent uses forecasted flightspecific context; and measured network-wide context. The reward uses components that trade off the decrease in on-time performance due to the hold decisions, for a decrease in missed connections. We attribute the global rewards to individual local hold actions through a novel delay tree that approximates the network interactions. Multiple flights are handled through the same instance of the agent handling them in sequence with varying state information. We evaluate our approach for two different airlines with training and testing over a microsimulator that uses real-world data for calibration. Across different algorithms (DQN, AC, A2C, DDPG), we find that the best performing RL-based agent is able to reduce significantly more (up to 50%) missed connections for a minimal decrease (5̃%) in on-time performance; when compared with a current rule-based heuristic. Further, the approach is tunable and able to transfer learn across different airlines.

To hold or not to hold? - Reducing Passenger Missed Connections in Airlines using Reinforcement Learning