Application Of Data Fusion And Machine Learning To The Analysis Of The Relevance Of Recommended Flight Reroutes

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). Traffic Management Initiatives, 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 operate with the flow of traffic, 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. In addition, because traffic in the National Airspace System can be forecasted through airline schedules and flight plans, it is also possible to predict the issuance of volume-related reroute advisories. Consequently, the objectives of this work is two-fold: 1) Assess the relevance of existing recommended reroutes, and 2) predict the issuance and the type of volume-related reroute advisories. This was achieved by 1) fusing data from relevant datasets, extracting statistics, and identifying trends and patterns within the data, and 2) developing models to predict the issuance of volume-related reroute advisories. It is expected that the capabilities developed may ultimately contribute to reducing unnecessary flight reroutes