Analyzing the Impacts of Inbound Flight Delay Trends on Departure Delays Due to Connection Passengers Using a Hybrid RNN Model

Abstract

Some delay patterns are correlated to historical performance and can reflect the trend of delays in future flights. A typical example is the delay from an earlier inbound flight causing delayed departure of a connecting and downstream outbound flight. Specifically, if an arriving aircraft arrives late, the connecting airline may decide to wait for connecting passengers. Due to the consistent flow of passengers to various destinations during a travel season, similar delay patterns could occur in future days/weeks. Airlines may analyze such trends days or weeks before flights to anticipate future delays and redistribute resources with different priorities to serve those outbound flights that are likely to be affected by feeder delays. In this study, we use a hybrid recurrent neural network (RNN) model to estimate delays and project their impacts on downstream flights. The proposed model integrates a gated recurrent unit (GRU) model to capture the historical trend and a dense layer to capture the short-term dependency between arrival and departure delays, and, then, integrates information from both branches using a second GRU model. We trained and tuned the model with data from nine airports in North, Central, and South America. The proposed model outperformed alternate approaches with traditional structures in the testing phase. Most of the predicted delay of the proposed model were within the predefined 95% confidence interval. Finally, to provide operational benefits to airline managers, our analysis measured the future impact of a potentially delayed inbound feeder, (PDIF) in a case study, by means of identifying the outbound flights which might be affected based on their available connection times (ACTs). From an economic perspective, the proposed algorithm offers potential cost savings for airlines to prevent or minimize the impact of delays.