Causal Effects of Landing Parameters on Runway Occupancy Time using Causal Machine Learning Models

Limited runway capacity is a common problem faced by most airports worldwide. The two important factors that affect runway throughput are the wake-vortex separation and Runway Occupancy Time (ROT). Therefore, to improve runway throughput, Wake Turbulence Re-categorisation program (RE-CAT) was introduced to reduce the minimum separation distance required between successive aircraft on final approach. As a result, the constraining impact of ROT on runway throughput has now become significant. The objective of this paper is to identify data-driven intervention to reduce the ROT of landing aircraft. Specifically, we propose a data- driven approach to estimate the causal effect of landing parameters on ROT. We propose categorisation of each landing parameter into groups using Gaussian process models and employ Generalised Random Forest (GRF) to estimate the average treatment effect and the standard deviation of each landing parameters. Experimental results show that a few procedural changes to current lauding procedure may reduce ROT. The results establish that slowing down the aircraft speed in the final approach phase leads to shorter ROT. In the final approach phase, ROTs of aircraft which are at least 10 knots slower than the average aircraft speed are on an average 2.63 seconds shorter. Furthermore, aircraft that arc at least 10 knots faster than the average aircraft have on average 4 seconds longer ROTs. The second finding of this work is that flexible glide-slope angles should he introduced for the different aircraft types to achieve better ROT performance. Therefore, our findings also validate the industry need for Ground-Based Augmented System landing system which provides landing guidance with flexible glide-slopes.