A visualization and optimization of the impact of a severe weather disruption to an air transportation network

The task of visualizing data becomes more challenging as the size and complexity of the data increases. Specifically, for Air transportation networks, large data sets or Big Data contain temporal and spatial information that can facilitate decision-making during disruptions. This research explores the interpretation and visualization of time-dependent flight and weather data. We focus on visualizations that characterize the impact of severe weather disruptions to the Air transportation network with respect to traffic flow capacity and route connectivity. We include a binary integer programming optimization model to propose scheduling options that minimize delays for the disrupted flights. This study analyzes airline and weather Big Data during a severe weather event and highlights air traffic management options.