AIRSPACE PLANNING FOR OPTIMAL CAPACITY, EFFICIENCY, AND SAFETY USING ANALYTICS
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Air Navigation Service Providers (ANSP) worldwide have been making a considerable effort for the development of a better method for planning optimal airspace capacity, efficiency, and safety. These goals require separation and sequencing of aircraft before they depart. Prior approaches have tactically achieved these goals to some extent. However, dealing with increasingly congested airspace and new environmental factors with high levels of uncertainty still remains the challenge when deterministic approach is used. Hence due to the nature of uncertainties, we take a stochastic approach and propose a suite of analytics models for (1) Flight Time Prediction, (2) Aircraft Trajectory Clustering, (3) Aircraft Trajectory Prediction, and (4) Aircraft Conflict Detection and Resolution long before aircraft depart. The suite of data-driven models runs on a scalable Data Management System that continuously processes streaming massive flight data to achieve the strategic airspace planning for optimal capacity, efficiency, and safety. (1) Flight Time Prediction. Unlike other systems that collect and use features only for the arrival airport to build a data-driven model for predicting flight times, we use a richer set of features along the potential route, such as weather parameters and air traffic data in addition to those that are particular to the arrival airport. Our feature engineering process generates an extensive set of multidimensional time series data which goes through Time Series Clustering with Dynamic Time Warping (DTW) to generate a single set of representative features at each time instance. The features are fed into various regression and deep learning models and the best performing models with most accurate ETA predictions are selected. Evaluations on extensive set of real trajectory, weather, and airport data in Europe verify our prediction system generates more accurate ETAs with far less variance than those of European ANSP, EUROCONTROL’s. This translates to more accurately predicted flight arrival times, enabling airlines to make more cost-effective ground resource allocation and ANSPs to make more efficient flight scheduling. (2) Aircraft Trajectory Clustering. The novel divide-cluster-merge; DICLERGE system clusters aircraft trajectories by dividing them into the three standard major flight phases: climb, en-route, and descent. Trajectory segments in each phase are clustered in isolation, then merged together. Our unique approach also discovers a representative trajectory, the model for the entire trajectory set. (3) Aircraft Trajectory Prediction. 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 account. 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. The experiments use a real trajectory dataset with pertaining weather observations and demonstrate the effectiveness of our approach to the trajectory prediction process for Air Traffic Management. (4) Aircraft Conflict Detection. We propose a novel data-driven system to address a long-range aircraft conflict detection and resolution (CDR) problem. Given a set of predicted trajectories, the system declares a conflict when a protected zone of an aircraft on its trajectory is infringed upon by another aircraft. The system resolves the conflict by prescribing an alternative solution that is optimized by perturbing at least one of the trajectories involved in the conflict. To achieve this, the system learns from descriptive patterns of historical trajectories and pertinent weather observations and builds a Hidden Markov Model (HMM). Using a variant of the Viterbi algorithm, the system avoids the airspace volume in which the conflict is detected and generates a new optimal trajectory that is conflict-free. The key concept upon which the system is built is the assumption that the airspace is nothing more than a horizontally and vertically concatenated set of spatio-temporal data cubes where each cube is considered as an atomic unit. We evaluate the system using real trajectory datasets with pertinent weather observations from two continents and demonstrate its effectiveness for strategic CDR. Overall, in this thesis, we develop a suite of analytics models and algorithms to accurately identify current patterns in the massive flight data and use these patterns to predict future behaviors in the airspace. Upon prediction of a non-ideal outcome, we prescribe a solution to plan airspace for optimal capacity, efficiency, and safety.