Airport surface gridlock analysis: A case study of Chicago O'Hare 2007

Abstract

The evolution of Air Traffic Control (ATC) and Traffic Flow Management (TFM) over the last decade has resulted in improved coordination of departures and enroute trajectories to minimize airborne congestion and airborne holding. Since the system is a “closed-system,” the effect of these changes has been to shift airborne holding, to holding flights on the airport surface at the origin. This has placed increased emphasis on the coordination of gates, remote stands, airport parking, and surface trajectories. Previous research in this area has evaluated surface operations at specific airports through manual observation and/or surface track data, and through aggregate airport data and air traffic control logs. The purpose of this research is to develop a tool to systematically measure airport surface gridlock using publicly available data over extended periods of time (e.g. one year). The tool uses ASPM and AOTP data to count the number of aircraft on the surface but not at the gates (m i ). A measure of surface gridlock for a day is determined by identifying the number of flights on the surface (but not at the gates) for each 15-minute period in a 24 hour day. The maximum, mean, and median 15 minute flight count for a day is used to characterize the airport performance for a 24 hour period. A case study analysis of flight operations at Chicago O'Hare airport in 2007 identified the number of flights on the surface (but not at a gate) for each 15-minute period. ORD experienced an average in maximum 15-minute surface flight count of 97 flights with a standard deviation of 18 flights. Twenty-four days in the year experienced a maximum surface flight count in excess of 1.5 times the standard deviation (≈ 125 flights). Fifteen days in the year experienced a maximum surface flight count in excess of 2 times the standard deviation (≈ 134 flights). Eight days in the year experienced a maximum surface flight count in excess of 3 times the standard deviation (≈ 150 flights). The maximum surface flight count in excess of twice the standard deviation is concentrated in the summer convective season, and the winter snow season. Detailed analysis of the operations when the maximum surface flight count was in excess of three times the standard deviation identifies irregular operations with significant reductions in operations due to cancellations. However the coordination between arrivals, gates, and departures results in surface congestion. These results suggest that modernization initiatives that address normal operations are unlikely to affect these irregular operations.