Aircraft Trajectory Prediction made easy with Predictive Analytics
Samet Ayhan*, University of Maryland; Hanan Samet, University of Maryland
At the heart of Air Traﬃc Management (ATM) lies the Decision Support Systems (DST) that rely upon accurate trajectory prediction to determine how the airspace will look like in the future to make better decisions and advisories. Dealing with airspace that is prone to congestion due to environmental factors still remains the challenge especially when a deterministic approach is used in the trajectory pre-diction process. In this paper, we describe a novel stochastic trajectory prediction approach for ATM that can be used for more eﬃcient and realistic ﬂight planning and to assist airspace ﬂow management, potentially resulting in higher safety, capacity, and eﬃciency commensurate with fuel savings thereby reducing emissions for a better environment.
Our approach considers airspace as a 3D grid network, where each grid point is a location of a weather observation. We hypothetically build cubes around these grid points, so the entire airspace can be considered as a set of cubes. Each cube is deﬁned by its centroid, the original grid point, and associated weather parameters that remain homogeneous within the cube during a period of time. Then, we align raw trajectories to a set of cube centroids which are basically ﬁxed 3D positions independent of trajectory data. This creates a new form of trajectories which are 4D joint cubes, where each cube is a segment that is associated with not only spatio-temporal attributes but also with weather parameters. Next, we exploit machine learning techniques to train inference models from historical data and apply a stochastic model, a Hidden Markov Model (HMM), to predict trajectories taking environmental uncertainties into ac-count. During the process, we apply time series clustering to generate input observations from an excessive set of weather parameters to feed into the Viterbi algorithm. Our experiments use a real trajectory dataset with pertaining weather observations and demonstrate the eﬀectiveness of our approach to the trajectory prediction process for ATM.
Filed under: Time Series and Stream Mining