Exploring prediction accuracy for optimal taxi times in airport operations using various machine learning models

Understanding delay conditions and making accurate predictions are essential for optimizing turnaround and taxi times, which in turn reduces fuel consumption and lowers CO 2 emissions in airport operations. However, while existing research has explored the impact of various prediction models on airport operations, it often overlooks the performance of Collaborative Decision Making (CDM) variables when discussing delay conditions. The implementation of CDM at major European airports has led to a milestone-based approach within airport operations, particularly in the turnaround operations, segmenting these operations with unique features. The purpose of this paper is to systematically investigate the efficacy of various machine learning techniques, such as linear regression, regression trees, random forests, elastic nets, and multi-layer perceptrons (MLP), in accurately predicting delay categories within the CDM framework. For this purpose, we analyzed CDM operational data from Madrid Airport, with at least 166,185 flight observations. Our findings illustrate a training methodology on how different models vary in prediction accuracy when applied to CDM operational data. We applied the SHAP (SHapley Additive exPlanations) method for feature importance analysis of all our independent variables to interpret the output of our machine learning models. Our results indicate that linear regression and elastic nets are the most effective machine learning models for achieving high prediction accuracy within the CDM framework. To test their robustness, we extended the analysis with predictions for better schedule times for taxi times on arrival and depature for selected runways using a different dataset. Our results contribute by showcasing a training methodology, highlighting how elastic net model as the best-performing model can be adopted for turnaround operations. In conclusion, we discuss the implications of our results for runway demand policies and use of airport resources such as gate & runaway allocation. • Collaborative Decision Making (CDM) employs a milestone-based approach that introduces new performance indicators. • Existing research often overlooks CDM features when discussing delay conditions with machine learning. • This study investigates how five distinct machine learning methods. • Subsequently, we expanded our analysis to predict taxi times using a different dataset. • We conclude the paper by discussing the implications of our findings.