Semantic-aware aircraft trajectory prediction using flight plans

Abstract

Aircraft trajectory prediction (TP) is a challenging and inherently data-driven time-series modeling problem. Adding annotation or enrichment parameters further increases the search space complexity, especially when ‘blind’ optimization algorithms are employed. In this paper, flight plans, localized weather and aircraft properties are introduced as trajectory annotations that enable modeling in a space higher than the typical 4-D spatio-temporal. A multi-stage hybrid approach is employed for a new variation of the core TP task, the so-called Future Semantic Trajectory Prediction, including clustering the enriched trajectory data using a semantic-aware similarity function as distance metric. Subsequently, a separate predictive model is trained for each cluster, using a nonuniform graph-based grid that is formed by the waypoints of each flight plan. In practice, flight plans constitute a constrained-based training of each predictive model, one for each waypoint, independently. The proposed method is formulated and experimentally validated with real aviation dataset (flight plans and IFS radar tracks) and localized weather data for a 1-month time frame of flights in the Spanish airspace. Various types of predictive models are tested, including hidden Markov model (HMM), linear regressors, regression trees and feed-forward neural networks. The results show very narrow confidence intervals for the per-waypoint TP errors in HMM, while the more efficient linear and nonlinear regressors exhibit 3-D spatial accuracy much lower than the current state of the art, up to a factor of five compared to ‘blind’ TP for complete flights, in the order of 2–3 km compared to the actual flight routes.