Graph Neural Networks with Spatiotemporal Flow Features for Aircraft Taxi-Out-Time Prediction

This paper presents a framework for modeling and predicting impeded aircraft taxi-out times based on machine learning techniques. The presented framework can be integrated into departure management systems to support the pretactical/tactical planning of departure movements and the optimization of airport resources. The taxi-out time is modeled with two components: the time taken to travel from the gate to the departure queue and the time spent in the departure queue. The first component (termed the taxiing time) is mainly affected by surface traffic conditions, while the latter component (termed the queuing time) can be more accurately modeled using characteristics derived from the departure queue. To model the spatiotemporal dependencies on traffic flow, we represent the airport taxi system as a node-link model. Flow features are derived in the form of edge attributes based on route information and movement start times. Departure trajectories utilize the same node-link representation, in the form of a subgraph incorporating additional operationally available information. The taxi-out time of each trajectory is obtained by processing the subgraph using a graph neural network (GNN) with transformer layers. Predictions from the GNN model are compared against standard methodologies by the Federal Aviation Administration (FAA) and EUROCONTROL, as well as against predictions made by gradient boosting machines (GBM), a popular decision-tree-based machine learning technique. Results show that both GNN and GBM models outperform the standard FAA and EUROCONTROL methods (with the prediction errors of the former group lower by 40–60% relative to the latter), and the novel GNN model outperforms the GBM model by a considerable margin of approximately 8 s, translating to a 10% improvement in model performance of the GNN model relative to the GBM model.