Objective and automatic estimation of excess taxi-times

Abstract

This paper describes the methodology developed at Sensis Corporation for the automatic and objective estimation of total and excess taxi-times from Airport Surface Detection Equipment - Model X (ASDE-X) surveillance data, such that these quantities can be conditioned on the basis of runway and gate/ramp locations. For each airport in the daily summary, we report the number of arrival and departure operations, total taxi-time, excess taxi-time, percent of known aircraft types, and the percent of complete aircraft taxi trajectories. Other data columns in the daily summary include fuel burn, fuel cost, and emissions (i.e., HC, CO, NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> ), reported as total and excess quantities. A daily report is automatically generated for the airports at which Sensis Corporation currently makes recordings: ATL, BDL, CLT, DTW, IAD, MCO, MEM, MKE, ORD, PVD, SDF, SEA, and STL; this list will grow as more ASDE-X systems are fielded. Estimation of excess fuel burn and cost requires data on the aircraft type and excess taxi-time. The aircraft type determines the fuel burn rate, taken from the ICAO database; the excess taxi-time depends on a complete taxi trajectory in the movement area. The percent of known fuel burn rates ranges from 85 to 94% for the current set of airports. The percent of complete trajectories ranges from 83 to 93% for taxiing in the movement area. For validation, we have undertaken comparison of operation counts from the processing of ASDE-X data with data reported in the FAA's Aviation System Performance Metrics (ASPM) database, and have found good agreement (standard error < 1 operation). Also, we have performed some comparisons of the ASDE-X total-time estimates against the reportable quantities from the on-time performance database of the Department of Transportation (DOT) Bureau of Transportation Statistics (BTS). This analysis is performed on a per-aircraft basis by matching the tail numbers and out-off-on-in (OOOI time) events between the two data sets.