Predicting Estimated Time of Arrival for Commercial Flights

Abstract

Unprecedented growth is expected globally in commercial air traffic over the next ten years. To accommodate this increase in volume, a new concept of operations has been implemented in the context of the Next Generation Air Transportation System (NextGen) in the USA and the Single European Sky ATM Research (SESAR) in Europe. However, both of the systems approach airspace capacity and efficiency deterministically, failing to account for external operational circumstances which can directly affect the aircraft's actual flight profile. A major factor in increased airspace efficiency and capacity is accurate prediction of Estimated Time of Arrival (ETA) for commercial flights, which can be a challenging task due to a non-deterministic nature of environmental factors, and air traffic. Inaccurate prediction of ETA can cause potential safety risks and loss of resources for Air Navigation Service Providers (ANSP), airlines and passengers. In this paper, we present a novel ETA Prediction System for commercial flights. The system learns from historical trajectories and uses their pertinent 3D grid points to collect key features such as weather parameters, air traffic, and airport data along the potential flight path. The features are fed into various regression models and a Recurrent Neural Network (RNN) and the best performing models with the most accurate ETA predictions are compared with the ETAs currently operational by the European ANSP, EUROCONTROL. Evaluations on an extensive set of real trajectory, weather, and airport data in Europe verify that our prediction system generates more accurate ETAs with a far smaller standard deviation than those of EUROCONTROL. This translates to smaller prediction windows of flight arrival times, thereby enabling airlines to make more cost-effective ground resource allocation and ANSPs to make more efficient flight schedules.