Aircraft trajectory prediction and time estimation adapting social LSTM

The rapid expansion of the air transport industry, particularly at regional airports, necessitates the development of cost-effective trajectory prediction systems to manage increasing traffic demands. Also, a more precise planning at airports, e.g. for less resource intensive turnaround processes, is needed due to cost pressure. This relies on early knowledge of deviations from original flight schedule (scheduled time of arrival, STA). Early knowledge about changes in estimated time of arrival (ETA) is required for timely adjustment of the planning. So, accurate prediction of aircraft trajectories and ETA is a key operational requirement for regional and smaller airports that often lack advanced decision-support systems. In such environments, even small timing deviations can propagate through arrival and departure sequences (e.g., due to tight turnaround windows), affecting scheduling efficiency, runway utilization and turnaround processes. This work investigates the adaptation of a Social LSTM network (originally designed for pedestrian-motion prediction) to model aircraft lateral trajectory evolution and ETA forecasting. The proposed framework learns “interaction patterns between nearby aircraft”. In a second version, it learns as well the influence of meteorological conditions represented by Feature Joint Cubes. Experimental results on ADS-B data from the NRW region demonstrate that the Social LSTM reduces mean positional and ETA errors compared with a baseline Vanilla LSTM, particularly in complex traffic or weather situations.