Fundamental Framework to Plan 4D Robust Descent Trajectories for Uncertainties in Weather Prediction

Abstract

Aircraft trajectory planning is affected by various uncertainties. Among them, those in weather prediction have a large impact on the aircraft dynamics. Trajectory planning that assumes a deterministic weather scenario can cause significant performance degradation and constraint violation if the actual weather conditions are significantly different from the assumed ones. The present study proposes a fundamental framework to plan four-dimensional optimal descent trajectories that are robust against uncertainties in weather-prediction data. To model the nature of the uncertainties, we utilize the Global Ensemble Forecast System, which provides a set of weather scenarios, also referred to as members. A robust trajectory planning problem is constructed based on the robust optimal control theory, which simultaneously considers a set of trajectories for each of the weather scenarios while minimizing the expected value of the overall operational costs. We validate the proposed planning algorithm with a numerical simulation, assuming an arrival route to Leipzig/Halle Airport in Germany. Comparison between the robust and the inappropriately-controlled trajectories shows the proposed robust planning strategy can prevent deteriorated costs and infeasible trajectories that violate operational constraints. The simulation results also confirm that the planning can deal with a wide range of cost-index and required-time-of-arrival settings, which help the operators to determine the best values for these parameters. The framework we propose is in a generic form, and therefore it can be applied to a wide range of scenario settings.

A Short-Term Traffic Flow Prediction Method for Airport Group Route Waypoints Based on the Spatiotemporal Features of Traffic Flow

“…The output sequence length of the model in this paper is 4, predicting the future one-hour route point traffic flow. By testing different input sequence lengths of the model (4,8,12,16,20), it allows us to compare the prediction results to find the optimal input sequence length.…”

“…These deep learning methods utilize various models such as T-GCN [7], STS-DGCN [8], Location-GCN [9], AFMSTGCN [10], DGC-GRU [11], Bi-AGGCN [12], ConvLSTM [13], SCLN-TTF [14], AG2S-Net [15], AAGC-GRU [16], improved Cao method [17], ATFPNet [18], etc. By considering issues, such as spatiotemporal dynamic correlation mining of traffic flow [19] and external factors (weather [20], holidays), these methods construct congestion indices [21], delay indices [22], and employ various techniques to predict traffic flow [23] and congestion indices.…”

Dviejų orlaivių skrydžio konflikto situacijų modeliavimas, vertinant determinuotus ir stochastinius parametrus

“…Aptikus orlaivių konfliktinę situaciją, kiti konfliktinės situacijos, nagrinėjamos neidealioje (realioje) aplinkoje, sprendimo veiksniai yra neapibrėžtumai, tokie kaip vėjo kryptis ir greitis, orlaivių nepastovūs greičiai (Hoy & Boeing Company, 2016;Kamo et al, 2022;Courchelle et al, 2019).…”