Aircraft proximity maps based on data-driven flow modeling

With the forecast increase in air traffic demand over the next decades, it is imperative to develop tools to provide traffic flow managers with the information required to support decision making. In particular, decision-support tools for traffic flow management should aid in limiting controller workload and complexity, while supporting increases in air traffic throughput. To date, many decision-support tools exist to provide strategic, operational, and tactical decision-information for managing aircraft counts within airspaces across the National Airspace System. Lacking, however, are tools that address the operational needs in describing the spatial distribution and complexity of air traffic within a center for medium- and long-term planning horizons of 30 min and beyond. This paper seeks to address this gap through the introduction of three-dimensional aircraft proximity maps that evaluate the future probability of presence of aircraft at any given point of the airspace. Three types of proximity maps are presented: 1) presence maps that indicate the local density of traffic, 2) conflict maps that highlight locations of potential conflicts and their corresponding probabilities, and 3) outlier proximity maps that give an image of the probability of close proximity between aircraft belonging and aircraft not belonging (so-called "outliers") to dominant flows of traffic. These maps provide traffic flow managers with information relating to the complexity and difficulty of managing an airspace. The intended purpose of the maps is to anticipate how aircraft flows interact, and how outliers impact the dominant traffic flow for a given time period. The proximity maps are able to predict which critical regions may be subject to potential conflicts between aircraft, thereby requiring careful monitoring and additional effort to manage the airspace by air traffic controllers. The proximity maps are created using a generative air traffic flow model. Time-varying flow characteristics, such as the geometric configuration, aircraft speed distributions, and aircraft spatial distributions within the flow, are determined from archived Enhanced Traffic Management System data with support of a tailored clustering algorithm. The use of the maps is presented on a couple of "what-if" scenarios. Copyright © 2011 by the American Institute of Aeronautics and Astronautics, Inc.