Distance Measure to Cluster Spatiotemporal Scenarios for Strategic Air Traffic Management

Abstract

Convective weather, recognized as the leading contributor to delays in the National Airspace System, causes demand–capacity imbalance in the airspace. Strategic air traffic management aims to resolve the imbalance for offnominal conditions (for example, convective weather events) through redistributing flows and resources at the strategic timeframe (2–15 h in advance). As a component of scenario-driven strategic air traffic management, this paper develops a multiresolution spatiotemporal distance measure that, combined with standard distance-based clustering algorithms, can be used to group a wide range of possible weather-impact scenarios into a few representative clusters to facilitate corresponding management strategy design. Motivations of this new distance measure, its generation algorithm, and parameter impact analysis are described in detail to facilitate practical implementation and subsequent use in decision support. This multiresolution spatiotemporal distance measure not only addresses the needs of scenario-driven strategic air traffic management but also generally applies to broad data-driven decision support applications that involve large-scale physical processes of spatiotemporal spread dynamics, as the measure captures the similarity between scenarios of spatiotemporal spread patterns of varying shape, size, location, and intensity; corrects the “boundary effects”; and is flexible to a variety of data features in temporal and spatial dimensions. Performance evaluation is conducted through comparative studies and sensitivity analysis, using real weather-impact datasets.