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With the forecast increase in air traffic demand over the next decades, it isimperative to develop tools to provide traffic flow managers with theinformation required to support decision making. In particular,decision-support tools for traffic flow management should aid in limitingcontroller workload and complexity, while supporting increases in air trafficthroughput. While many decision-support tools exist for short-term trafficplanning, few have addressed the strategic needs for medium- and long-termplanning for time horizons greater than 30 minutes. This paper seeks to addressthis gap through the introduction of 3D aircraft proximity maps that evaluatethe future probability of presence of at least one or two aircraft at any givenpoint of the airspace. Three types of proximity maps are presented: presencemaps that indicate the local density of traffic; conflict maps that determinelocations and probabilities of potential conflicts; and outliers maps thatevaluate the probability of conflict due to aircraft not belonging to dominanttraffic patterns. These maps provide traffic flow managers with informationrelating to the complexity and difficulty of managing an airspace. The intendedpurpose of the maps is to anticipate how aircraft flows will interact, and howoutliers impact the dominant traffic flow for a given time period. Thisformulation is able to predict which "critical" regions may be subject toconflicts between aircraft, thereby requiring careful monitoring. Theseprobabilities are computed using a generative aircraft flow model. Time-varyingflow characteristics, such as geometrical configuration, speed, and probabilitydensity function of aircraft spatial distribution within the flow, aredetermined from archived Enhanced Traffic Management System data, using atailored clustering algorithm. Aircraft not belonging to flows are identifiedas outliers.

Aircraft Proximity Maps Based on Data-Driven Flow Modeling