For many metropolitan areas in U.S., flight traffic is distributed between multiple airports that service the region. Since 2008, there are trends showing that in certain multi-airport regions, flight traffic is being consolidated toward one “dominant” airport (as in LA) where as in other regions, departure traffic is becoming more evenly distributed between the multiple airports. The purpose of this study is to examine the effects these trends in departure volume are having on overall departure delay times in these regions. For this analysis, data from the airports in Los Angeles, San Francisco, New York, Washington, D.C. and Chicago are included. Based on the results of this research, there is evidence that the dominant hub model as evidenced in LA with LAX, is preferable when managing delays to a more dispersed model showcased in DC.

Deregulation of the airlines in the United States spawned a free-for-all system which led to a variety of agents within the aviation system all seeking to optimize their own piece of the aviation system, and the net result was that the aviation system itself was not optimized in aggregate, frequently resulting in delays. Research on the efficiency of the system has likewise focused on the individual agents, primarily focusing on the municipalities in an economic context, and largely ignoring the consumer. This paper develops the case for a systemic efficiency measurement which incorporates the interests of the airlines and the consumers with those of the airport operating municipalities in three different Data Envelopment Analysis (DEA) models: traditional Charnes-Cooper-Rhodes and Banker-Charnes-Cooper models, and a Directional Output Distance Function model, devised and interpreted using quality management principles. These models were combined to allow the resulting efficiencies of the operating configurations of the given airport to predict the efficiency of the associated airport. Based upon regression models, these efficiency measurements can be used as a diagnostic for improving the efficiency of the entire United States airspace, on a systemic basis, at the individual airport configuration level. An example analysis using this diagnostic is derived in the course of the development and description of the diagnostic and two additional case studies are presented.