Using ADS-B big data pattern analysis to improve the quality of multivariate 4D trajectory optimization strategies

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A highly efficient and safe air transport system relies on individually and system-wide optimized aircraft trajectories, where optimization functions typically focus on fuel, flight time minimization, transport quality, and environmental impact as major market drivers, or a combination thereof. Given these multiple criteria and the large-scale of the air transportation system, the trajectory building process for modern, efficient air transportation is highly computationally expensive, as its optimization relies on accurate aerodynamic and engine characteristics to be reflected in aircraft performance models. Moreover, environmental uncertainty sources should be statistically taken into consideration in the trajectory building process searching for individually optimized altitude, path and time conditions.This project aims to identify the benefits of using big data analysis and data driven methods for efficient trajectory and network design. So far used trajectory optimization tools are mostly analytically driven, model/search-based, and largely neglect historical trajectories as background. The rise of performance mass data gathering and storage capabilities, data mining over historical flight data is a novel candidate for robust and fast optimization. Given ICAO’s mandate on aircraft ADS-B (Automatic Dependent Surveillance - Broadcast) equipment installation by 2020, an aircraft tracking surveillance technology, increasing numbers of commercial aircraft are now being equipped with this capability. Throughout this project, we aim to better understand the suitability of applying big data-driven techniques for predicting single (local optimum) but also multiple (global optimum) aircraft trajectories. The biggest challenge in this approach is how to process these large quantities of data, more than 200,000 messages per second over a period of more than one year. Based on our initial joint research on compressed storage and indexing techniques, we intend to first search for efficient coupling of both partners’ strengths for single flight aircraft trajectory augmentation based on historic ADS-B track data in large entities and aircraft performance models for specific aircraft-engine combinations, then extending the optimization goal towards flow oriented objective functions reflecting particularly on small and large airline networks. Validation of the claimed model will be performed along significantly different styles and behavior in the Chinese and European airspace structure and market strategies guiding both airline network structures differently. Based on our research outcome, it will be possible to turn historical knowledge into scenario-based recommendations, which yield unprecedented accuracy and efficiency for the data-driven, goal-oriented design and optimization of aircraft trajectories.