Major U.S. airports are critical nodes in the air transportation network,providing the interface between ground and air transportation. Airports aregeographic monopolies with multiple stakeholders. Government regulationsrequire them to operate as public utilities under profit-neutral financial conditions.By their nature, the airport stakeholders have different and sometimes conflictingperformance objectives.Since U.S. airports operate under profit-neutral regulations, enterpriseperformance cannot be measured using traditional financial objectives and mustinstead be evaluated based on the airports’ ability to meet the objectives of all oftheir stakeholders. Comparative benchmarking is used for evaluating the relativeperformance of airports.An analysis of past benchmarks of airport performance described in thisdissertation shows that these benchmarks are ambiguous about which stakeholders’needs they address and provide limited motivation for why particular performancemetrics were used. Furthermore, benchmarks of airport performance use data ofmultiple dimensions, and such benchmarking without knowledge of utility functionsrequires the use of multi-objective comparison models such as Data EnvelopmentAnalysis (DEA). Published benchmarks have used different DEA model variationswith limited explanation of why the models were selected. The choices ofperformance metrics and the choice of DEA model have an impact on the benchmarkresults. The limited motivation for metrics and model render the publishedbenchmark results inconclusive.This dissertation describes a systematic method for airport benchmarking toaddress the issues described above. The method can be decomposed into threephases. The first phase is the benchmark design, in which the stakeholder goals andDEA model are selected. The selection of stakeholder goals is enabled by a model ofairport stakeholders, their relationships, and their performance objectives for theairport. The DEA model is selected using a framework and heuristics forsystematically making DEA model choices in an airport benchmark.The second phase is the implementation of the benchmark, in which thebenchmark data is collected and benchmark scores are computed. Benchmarkscores are computed using the implementation of DEA models provided in thedissertation.In the third phase, the results are analyzed to identify factors whichcontribute toward strong performance and poor performance, respectively, and toprovide recommendations to decision- and policy-makers.The benchmark method was applied in three case studies of U.S. airports:The first case study provided a benchmark of the level of domestic passengerair service to U.S. metropolitan areas. The frequency of service to hub airports andthe number of non-hub destinations served were measured in relation to the size ofthe regional economy and population. The results of this benchmark showed thatseven of 29 metropolitan areas have the highest levels of air service. Nine areas,including Portland, OR, San Diego, and Pittsburgh, have poor levels of air service.Contributing factors to poor levels of air service are the lack of airline hub service,limited airport capacity, and low airline yields.In the second case study, a benchmark of the degree of airport capacityutilization was conducted. The degree of capacity utilization at 35 major U.S.airports was evaluated as defined by the level of air service and volume ofpassengers carried in relation to the airport runway capacity. Seven out of 35airports have the highest levels of capacity utilization while six airports, includingHNL, PDX, and PIT, have poor levels of capacity utilization. Some airports with highlevels of airport capacity utilization incur large delay costs while the airports withpoor levels of utilization have excess capacity, indicating that funding for capacityimprovements should be directed away from the poorly performing airports tothose that are capacity constrained.The third case study recreated of an existing widely published benchmark.This analysis took the premise of a previously conducted benchmark that measuredairport efficiency and recreated it by applying the new benchmarking methodologyin two new component benchmarks:• A benchmark focused on the airports’ operating efficiency, usingparameters which included the number of passengers and aircraftmovements in relation to runway capacity and delay levels• A benchmark comparing the level of investment quality of theairports, using factors such as the debt service coverage ratio, theportion of origin and destination passengers, and the levels of non-aeronauticalrevenuesThe results of the new benchmark showed no statistically significantcorrelation with the results of the original benchmark, leading to a different set ofconclusions from the new benchmarks. This illustrates the importance of acomprehensive and systematic approach to the design of a benchmark.Practical implications of the analysis for policymakers relate to the allocationof funding for capacity improvement projects. Airports in some areas operate athigh levels of capacity utilization and provide high levels of air service for theirregions. These areas are at risk of not being able to satisfy continued growth in airtravel demand, limiting the potential for the areas’ future economic development.The most strongly affected area in this category is New York City. Similarly, theanalysis found areas where the current level of air service is limited due to airportcapacity constraints, including Philadelphia and San Diego. While airport capacitygrowth is subject to geographical and other restrictions in some of these areas,increased capacity improvement funds would provide a high return on investmentin these regions.In contrast, the analysis found that several airports with comparatively lowlevels of capacity utilization received funding for increased capacity in the form ofnew runway construction. These airports include Cleveland, Cincinnati, St. Louis,and Washington-Dulles.In light of this indication that improvement funding is currently not optimallyallocated, this benchmarking method could be used as a systematic, transparentmeans of enhancing the process of funding allocation.
