Prediction of airport surface potential conflict based on GNN-LSTM

Abstract

The development of the civil aviation industry has contributed to a steady increase in the number of daily flight operations at airports, which in turn has led to increasingly complex airport ground layouts. To aid airport managers in understanding the operational situation on the airport surface, this paper introduces a predictive model for airport ground conflict situations based on GNN-LSTM. This model identifies potential conflicts, conflict hotspots, and conflict hotspots zones, designating key intersections on taxiways as conflict hotspots according to taxiing rules. A conflict network is constructed, employing GNN with an integrated attention mechanism to extract structural features of the network, while LSTM is utilized to capture temporal features. After tuning the model parameters, predictions are made regarding the overall potential number of potential conflicts on the surface. To validate the effectiveness of the model, experimental analysis is conducted using AirTOp simulation data from Shenzhen Bao'an Airport, comparing GNN-LSTM model with GNN-GRU, LSTM, and GRU models, using RMSE and MAE as loss functions. The results demonstrate that he proposed modelling approach effectively extracts the temporal features of potential conflict and GNN-LSTM model outperforms other models in predicting the overall number of potential conflicts.