No abstract

Free Flight Phase 1 (FFP1) is an FAA program for improving the performance of the National Airspace System (NAS) through the deployment of advanced technologies for air traffic management. In addition to the deployment activities, FFP1 includes a significant evaluation component, which faces a significant hurdle. A plethora of factors—weather, demand, enhancements to the NAS infrastructure not related to FFP1, facility outages, and so on—may also cause changes in NAS performance. It is necessary to normalize for these factors in order to determine the effect of FFP1. Normalization procedures to isolate the impact of the implementation of an FFP1 technology—the Terminal Area Traffic Management Advisor (T-TMA)—are documented at the Southern California TRACON, where it is used for controlling traffic into Los Angeles International Airport. Two examples of normalization are presented. One examines the effect of T-TMA on airport arrival capacity, and the other looks at arrival delay. The results, although preliminary given the short time since implementation, are consistent: it appears that capacities have increased and delays decreased as a result of the deployment of the tool. Moreover, the magnitudes of the delay reductions and capacity increases are consistent.

(NASA) project. We first use an unexpected loss of surface surveillance data as an opportunity to gauge impacts. The analysis measures changes in taxi-out times, queue lengths, and departure rates before, during, and after the surveillance outage. We repeat the analysis comparing a baseline period before implementation of surface surveillance with a postimplementation period. Analyses of both data sets displays a reduction in taxi-out times and indicates an increase in effective departure capacity (approximately 5-10 aircraft per hour greater) during times when surveillance was available.

No abstract

One of the major difficulties in performing post-implementation analysis for an initiative is the lack of control over the environment. Many times, multiple initiatives are being attempted simultaneously and it is difficult to attribute operational impacts to specific programs. In this paper, we examine the taxi time and departure rate impacts of two recent enhancements at Orlando International Airport (MCO): a new runway and enhanced Air Traffic Control surface surveillance through the Airport Surface Detection Equipment-Model X (ASDE-X). Both enhancements have had positive effects on surface efficiency; however, each initiative exhibits unique impact characteristics when examined in relation to the surface demand. More specifically, plots of taxi-out time and departure rate vs. surface demand show quite different behaviors. We interpret the results and believe similar analysis should be useful for refining the estimated benefits of other ASDE-X sites and predicting the impact of future surface initiatives. The suggested methodology of examining the different trend characteristics of a metric in relation to demand should provide a valuable method for future post-implementation analysis.