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AIAA's Aircraft Technology, Integration, and Operations (ATIO) 2002 Technical 1-3 October 2002, Los Angeles, California

AIAA 2002-5866

Eric R. Mueller* and Gano B. Chatterji† NASA Ames Research Center, Moffett Field, CA 94035-1000 Abstract The increase in delays in the National Airspace System (NAS) has been the subject ofseveral studies in recent years. These reports contain delay statistics over the entire NAS, along with some data specific to individual airports, however, a comprehensive characterization and comparison of the delay distributions is absent. Historical delay data for these airports are summarized. The various causal factors related to aircraft, airline operations, change of procedures and trafficvolume are also discussed. Motivated by the desire to improve the accuracy of demand prediction in enroute sectors and at airports through probabilistic delay forecasting, this paper analyzes departure and arrival data for ten major airports in the United States that experience large volumes of traffic and significant delays. To enable such an analysis, several data fields for every aircraftdeparting from or arriving at these ten airports in a 21day period were extracted from the Post Operations Evaluation Tool (POET) database. Distributions that show the probability of a certain delay time for a given aircraft were created. These delay-time probability density functions were modeled using Normal and Poisson distributions with the mean and standard deviations derived from the raw data. Themodels were then improved by adjusting the mean and standard deviation values via a least squares method designed to minimize the fit error between the raw distribution and the model. It is shown that departure delay is better modeled using a Poisson distribution, while the enroute and arrival delays fit the Normal distribution better. Finally, correlation between the number of departures, numberof arrivals and departure delays is examined from a time-series modeling perspective. 1. Introduction An application of the Enhanced Traffic Management System (ETMS) is to provide an estimate of traffic demand at sectors and airports. The demand is computed based on airline schedule data, historical traffic data, filed flight plans, and radar track data1.

Host computer systems at the variousAir Route Traffic Control Centers (ARTCC’s) provide flight plan and radar derived time-stamped track positions to the ETMS. These data are used with flight plan-based trajectory models to predict the locations of both airborne aircraft and aircraft that are scheduled to depart. The forecast positions are used to project demand at airports, sectors, and fixes. For aircraft that are scheduled todepart in the future, departure time uncertainty is the major cause of demand prediction error; therefore increased departure time accuracy will directly increase the accuracy of such predictions. This study is motivated by the desire to improve the forecasting accuracy of departure times with a probabilistic delay time model. Since traffic management decisions are influenced by the predicteddemand, better demand forecasting is desirable. There have been attempts to improve forecasting by using alternative trajectory prediction methods in systems that are currently being developed such as the FAA/CAASD Collaborative Routing Coordination Tools (CRCT) program, NASA Future ATM Concepts Evaluation Tool (FACET), and the NASA/FAA Center TRACON Automation System (CTAS) based Traffic FlowAutomation System (TFAS).2-4 Masalonis, et. al.,5 summarizes the results of preliminary analysis of CRCT traffic prediction performance compared to the ETMS. The study reveals improvements in demand forecasting are possible over ETMS by modeling airspace restrictions. However, the predictability varies according to factors such as the type of sector and time horizon, irrespective of the trajectory...
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