Sustainable and data-driven airport operations: Optimisation models and machine learning approaches

The aerospace industry annually provides transport for billions of passengers along trillions of kilometers. The industry is continuously aiming to provide these services in a more efficient and sustainable way. One possibility is to consider improving airside airport operations, both current types and those expected in the near future. Scheduling airport operations requires taking into account flight planning, airport layout, routing requirements and personnel planning. Current operational planning is characterised by application of linear programming tools for strategic planning, and manual adjustment for adaptive planning.

This dissertation aims to develop data-driven optimisation models, to increase the efficiency and sustainability of various airside airport operations, and to apply these models to airport case studies. The focus is first put on external electric taxiing, a new taxiing technique using electric towing vehicles (ETVs) to tow aircraft from gates to runways and vice versa. Many airports are considering to implement this technique, as it offers a large improvement in reducing their greenhouse gas emissions, noise levels and air pollution, which is an improvement for passengers, airport personnel, and local residents.

The first goal is to create a comprehensive overview of the operational aspects of external electric taxiing, by reviewing existing research work and industry sources. This overview includes the expected specifications of ETVs and the future procedures for electric taxiing movement. Electric taxiing introduces a new airside operation to the airport: ETV-to-aircraft scheduling. Studies on this new operation, as well as on vehicle routing, vehicle fleet sizing and battery charging optimisation models, which are needed for electric taxiing, are reviewed. The overview also includes the remaining research challenges to achieve large-scale ETV implementation in the next few decades.

The second goal is to develop an optimisationmodel to performETV-to-aircraft scheduling that takes into account realistic airport circumstances. A more efficient ETV-toaircraft schedule, which allows more aircraft to be towed by an ETV fleet, will reduce airport emissions more. Some studies have already proposed ETV-to-aircraft scheduling models. However, they do not include all elements needed to make the model realistic and comprehensive, such as routing with conflict and collision avoidance, ETV charging and discharging, and airport surface movement specifications. Two more elements are added to this list in this work: airport electricity capacity and achieving a time-efficient model. Two models are developed for full-day ETV-to-aircraft scheduling, a Mixed-Integer Linear Programming (MILP) model and an Adaptive Large Neighbourhood Search (ALNS) model. Both models limit ETV charging to the electricity capacity of the airport. The ALNS model is able to create near-optimal full-day schedules for large fleet sizes within a few hours, for a large airport case study. The ALNS model is tested with various daily electricity capacity profiles, which shows the necessity of night charging and the effects of increasing amounts of charging during the day.

The third goal is to develop an optimisation approach to retain efficiency for electric taxiing in a real-time situation. The models developed for the second goal are applicable for strategic scheduling. During operation, disruptions to the strategic schedule will occur, and adaptive scheduling is required to continue operation. In this dissertation both a strategic and disrupted scheduling model are developed. The disrupted model reassigns delayed aircraft to ETV, aiming to minimize the changes to the original schedule. The model is used to create an adaptive schedule in a large airport case study using historical flight data. At the start of every half hour period, the disruptions due to flight delays of the next period are incorporated in a new schedule. The results show the efficacy of the disrupted model in minimizing schedule changes, which does not come at the expense of emission savings. In addition to electric taxiing, this dissertation focuses on improving the efficiency and robustness of airside operations by predicting airport disruptions, to avoid additional use of resources and to provide a better service. Where the previous part consists of using models to react to flight delays, operations can also be improved by predicting them in advance. In existing works, delays are predicted by classification or as point prediction. In this dissertation, probabilistic prediction is applied to flight delay, using two machine learning algorithms: Mixture Density Networks and Random Forests Regression. In addition, metrics suited to probabilistic prediction are developed and used to evaluate the algorithm performance. In a small airport case study, the algorithms are shown to be able to predict delays within a Continuous Ranked Probability Score (CRPS) of eleven minutes. The probabilistic prediction algorithms generate estimated delay distributions, which include extended uncertainty information. To illustrate the utility of the predictions for airport operations, they are applied in a probabilistic model aimed to increase the robustness of the flight-to-gate assignment problem. The proposed model is shown to reduce the number of gate-conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. The robustness of the assignment can be controlled with a model parameter.

Another method for predicting flight delays is binary classification, which is popular in literature. However, when posed as a binary problem, flight delay and also flight cancellation prediction suffer from a large data imbalance. This causes a distorted view when using metrics such as accuracy. This dissertation develops a systematic approach to binary prediction with imbalanced data, by considering a range of sampling ratios and various sampling techniques. Two machine learning algorithms are applied to a small airport historical flight dataset. The results underline the need to investigate the influence of varying data imbalance ratios on the performance of classification algorithms in various metrics.

Throughout this dissertation, the focus has been on improving the sustainability and efficiency of airport operations through data-driven approaches. These approaches include MILP models, heuristics and machine learning models. The developed models provide support for airport planners to improve current and future scheduling tasks. However, it remains future work to apply similar techniques to other airside operations and to further improve the realism and real-time usability of the current models. In addition, airports’ spatial planners, air traffic controllers and ETV developers will play a critical role in the further development and implementation of electric taxiing. Overall, this dissertation forms a starting point for airport planners aiming to use data-driven methods to improve the sustainability and efficiency of airports, to ensure more durable and reliable air transportation services.