Wheels-Off Time Estimation at Non-ASDE-X Equipped Airports

Abstract

This paper is devoted to the development and evaluation of wheels-off time estimation and selection of airports without advanced automation that could benefit from wheels-off time estimation. After eliminating non-hub, small-hub, and airports with Airport Surface Detection Equipment Model-X, 29 airports were selected for further analysis. Using taxi-out delay, traffic management initiative delay counts and commercial operation counts as metrics in a multiple-metric K-Means method, these airports were organized into three groups. San Jose International, Cleveland-Hopkins International and San Francisco International are recommended for development and testing of wheels-off time estimation methods as they are suitable representatives of these three groups. The second part of the paper is devoted to wheels-off time estimation using a simulation procedure that uses kinematic models of different types of aircraft and a node-link graph to simulate surface traffic. The central idea is to integrate the aircraft equations of motion along the path in the node-link graph while complying with separation constraints and rules for waiting to cross intersections and active runways. While the intent was to apply the wheels-off estimation method to San Jose, the geometry data and the surveillance data were not received in time for this study. Therefore, modeling, simulation and tests were done with Dallas-Fort Worth traffic for which the needed data were available. Of the two approaches described in the paper, the data-driven approach, which uses historical taxi-times, can be readily applied to airports like San Jose, Cleveland and San Francisco.

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

“…19,20 The SOSS model was also applied to estimate the wheels-off times at DFW, but it could not provide better estimation than a data-driven method in which average taxi times from actual data were added to the gate-out times for predictions. 21 Another example is LINear Optimized Sequencing (LINOS), which is a discrete event-based fast-time simulation tool developed by US Airways (now American Airlines). 22 LINOS can predict taxi times and surface congestion to increase awareness of the ramp controller by continuously evaluating the airport state.…”