The use of demand correlation in the modeling of air carrier departure delays as first-order autoregressive random processes

摘要

A method for modeling air carrier departure delays at commercial-service airports as autoregressive random processes is presented. This method employs the correlation of a priori demand data to significantly reduce prediction error in the optimal least-squares estimator for additive white noise. The reduction factor of the prediction error is demonstrated to be on the order of 10~2 over that of the unbiased estimator.