Capacity Variation Algorithms for Simulation Modeling and Performance Analysis

Abstract

Performance as measured by the standard Key Performance Indicators (KPIs) of delay, flight efficiency, on-time punctuality and predictability are directly influenced by the capacity available in the system. Capacity is increased with new technology and airport infrastructure. However, capacity also varies due to other short-term events such as weather or airport maintenance. These interdependencies complicate performance analysis by masking other effects such as changes in demand or longer term investment that improves the operation of the National Airspace System (NAS). An automated means of determining causal reasons for capacity variation is difficult and labor intensive as the causes may be site specific, due to weather events difficult to extract from traditional sources, or not recorded in a database that lends itself to automation. Simulation modeling and performance analysis that rely on ceiling and visibility will often under predict the effects of weather on capacity variation. Therefore, understanding other causal factors is a key step in determining inefficiencies that are actionable by Air Navigation Service Providers (ANSP) and aviation investment programs. This paper examines performance at airports determined to be most sensitive to capacity variation and determines which causal factors can be best automated by weather, demand and performance databases in use today. A set of weather rules are provided that show an improved correlation between METAR provided weather condition and Air Traffic Control (ATC) called rates than what is obtained using the more basic rules of ceiling and visibility. The paper concludes by estimating the improvements to NAS-wide simulation and performance modeling that could be realized by utilizing more detailed causal relationships of weather and capacity.