Application of data fusion and machine learning to the analysis of the relevancy of recommended flight reroutes

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Dard, Ghislain
Mavris, Dimitri N.
Pinon Fischer, Olivia
Paglione, Mike
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One of the missions of the Federal Aviation Administration (FAA) is to maintain the safety and efficiency of the National Airspace System (NAS). One way to do so is through Traffic Management Initiatives (TMIs). TMIs, such as reroute advisories, are issued by Air Traffic Controllers whenever there is a need to balance demand with capacity in the National Airspace System. Indeed, rerouting flights ensures that aircraft comply with the air traffic flow, remain away from special use airspace, and avoid saturated areas of the airspace and areas of inclement weather. Reroute advisories are defined by their level of urgency i.e. Required, Recommended or For Your Information (FYI). While pilots almost always comply with required reroutes, their decisions to follow recommended reroutes vary. Understanding the efficiency and relevance of recommended reroutes is key to the identification and definition of future reroute options. Similarly, being able to predict the issuance of volume-related reroute advisories would be of value to airlines and Air Traffic Controller (ATC). Consequently, the objective of this work was two-fold: 1) Assess the relevancy of existing recommended reroutes, and 2) predict the issuance and the type of volume-related reroute advisories. The first objective has been fulfilled by fusing relevant datasets and developing flights compliance metrics and algorithms to assess the compliance of flights to recommended reroutes. The second objective has been fulfilled by fusing traffic data and reroute advisories and then benchmarking Machine Learning techniques to identify the one that performed the best.
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