The Use of Demand Correlation in the Modeling of Air Carrier Departure Delays as First-Order Autoregressive Random Processes

This article presents a method for modeling air carrier departure delays at commercial-service airports using autoregressive random processes. This method employs the correlation of a priori demand data to significantly reduce prediction error in the optimal least-squares (OLS) estimator for additive white noise. The author determines the reduction factor of the prediction error to be on the order of 102 over that of the unbiased estimator. The author describes how the model is used for delay and demand data (from the Bureau of Transportation Statistics, or BTS) reflecting air carrier operations at five major commercial-service airports during 1 month of 2006. The author concludes that using this T estimator can provide air traffic managers another tool with which to combat the increasing problem of congestion within the system.

Language

  • English

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Filing Info

  • Accession Number: 01496520
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 5 2013 4:51PM