Airport Taxi Time Prediction and Alerting: A Convolutional Neural Network Approach

Taxi-out time is an indicator of departure efficiency and is often the early signal of large holding and diversion events for airports that have constrained surface space. This is one of the real-time performance metrics that is of great interest to air traffic managers and flight dispatchers. For a busy airport that has limited tarmac space like LaGuardia Airport (LGA), an increasing average taxi-out time under a deteriorating visibility condition could soon lead to surface gridlock that would cause significant delays to both arrivals and departures. Thus, research is needed to develop an early alert or prediction of long taxi-out times to enable early delay mitigation actions. The problem of predicting taxi-out times has received considerable treatment in the aviation literature. Most research exploring the domain of taxi time prediction has focused on predicting taxi-out times for individual aircraft. Inaccurate taxi-out times can lead to a variety of National Airspace System (NAS) inefficiencies, such as a reduction in predictability for downstream Traffic Flow Management (TFM) applications and excess fuel consumption after push back from the gate. By better predicting aircraft-specific taxi-out times, informed updates can be made to the flight schedule to improve predictability and more efficiently use available NAS resources (e.g., capacity). Although our focus is on predicting average taxi-out time, it's worth reviewing the literature on aircraft-specific taxi-out time predictions for historical context.