Using Data Science Techniques to Improve Runway Throughput

Abstract

Improving runway efficiency is a major goal of SESAR. There are a couple of potential solutions to reduce the necessary spacing between arrivals on final approach, which can lead to runway throughput improvement provided that other safety measures are still in force. In order to achieve this, data science can be used as well in general and machine learning algorithms in particular. The aim is to estimate the taxiway where the landing aircraft is going to vacate the runway so that the runway occupancy times associated with that taxiway could be used upon which separation minima is based. It is also important to estimate the probability of vacating the runway via the previously estimated taxiway to enable mitigation measures to work efficiently and in a timely manner to handle risk resulting from the reduction of spacing.