The Federal Aviation Administration (FAA) is primarily responsible for the advancement, safety, and regulation of civil aviation, as well as overseeing the development of the air traffic control system in the United States. As such, it is faced with tremendous amounts of data on a daily basis. This data, which comes in high volumes, in various formats, from disparate sources and at various frequencies, is used by FAA analysts and researchers to make accurate forecasts, improve the safety and operational performance of their operations, and streamline processes. However, by its very nature, aviation Big Data presents a number of challenges to analysts: it impedes their ability to get a real-time picture of the state of the system, identify trends and operational patterns, make real-time predictions, etc. As such, the overarching objective of the present effort is to support FAA through the development of a data fusion framework to support the analysis of aviation Big Data. For the purpose of this research, three datasets were considered: System-Wide Information Management (SWIM) Flight Publication Data Service (SFDPS), Traffic Flow Management System (TFMS), and Meteorological Terminal Aviation Routine (METAR). The equivalent of one day of data was retrieved from each dataset, parsed and fused. A use case was then used to illustrate how a data fusion framework could be used by FAA analysts and researchers. The use case focused on predicting the occurrence of weather-related Ground Delay Programs (GDP) at the Newark (EWR), La Guardia (LGA), and Boston Logan (BOS) International Airports. This involved developing a prediction model using the Decision Tree Machine Learning technique. Evaluation metrics such as Matthew's Correlation Coefficient were then used to evaluate the model's performance. It is expected that a data fusion framework, once integrated within the FAA's Computing and Analytics Shared Services Integrated Environment (CASSIE) could be used by analysts and researchers alike to identify trends and patterns and develop efficient methods to ensure that the U.S. civil and general aviation remains the safest in the world.
In recent years, the use of data mining and machine learning techniques for safety analysis, incident and accident investigation, and fault detection has gained traction among the aviation community. Flight data collected from recording devices contains a large number of heterogeneous parameters, sometimes reaching up to thousands on modern commercial aircraft. More data is being collected continuously which adds to the ever-increasing pool of data available for safety analysis. However, among the data collected, not all parameters are important from a risk and safety analysis perspective. Similarly, in order to be useful for modern analysis techniques such as machine learning, using thousands of parameters collected at a high frequency might not be computationally tractable. As such, an intelligent and repeatable methodology to select a reduced set of significant parameters is required to allow safety analysts to focus on the right parameters for risk identification. In this paper, a step-by-step methodology is proposed to down-select a reduced set of parameters that can be used for safety analysis. First, correlation analysis is conducted to remove highly correlated, duplicate, or redundant parameters from the data set. Second, a pre-processing step removes metadata and empty parameters. This step also considers requirements imposed by regulatory bodies such as the Federal Aviation Administration and subject matter experts to further trim the list of parameters. Third, a clustering algorithm is used to group similar flights and identify abnormal operations and anomalies. A retrospective analysis is conducted on the clusters to identify their characteristics and impact on flight safety. Finally, analysis of variance techniques are used to identify which parameters were significant in the formation of the clusters. Visualization dashboards were created to analyze the cluster characteristics and parameter significance. This methodology is employed on data from the approach phase of a representative single-aisle aircraft to demonstrate its application and robustness across heterogeneous data sets. It is envisioned that this methodology can be further extended to other phases of flight and aircraft.
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