The European aviation system is a key enabler in guaranteeing economic welfare and is mandatory to ensure reliable mobility between large cities and metropolises. In 2014, aviation supported 8.8 million jobs in the European Union and contributed over €621 billion to European Union Gross Domestic Product, say 4.7

Today, challenges in aviation are often related to the efficient handling of aircraft movements and airport operations in periods with high traffic volumes. During the last decade, airports have developed from simple infrastructure providers to high-performance companies. High quality of punctual and on-time flights has become a central element in goal definitions of airport operators. Further increase in performance, however, will be necessary to manage the predicted growth of passengers and flights. Using a simulation of the European aviation system, the effect of increased capacity at a local airport on the overall network performance is investigated using different scenarios. The results show significant potential to improve the network performance by enhancing a congested airport. A 5% capacity improvement at London Heathrow leads to 22% less inbound and 49% less outbound delay at the local airport. Nearly the same absolute amount of inbound delay can be saved at the other network airports due to benefit propagation effects. Further, the additional waiting time to use the runway decreases, and the punctuality of flights is improved.

Punctual and reliable aircraft ground handling operations at the airports significantly contribute to efficient traffic flows in the air traffic network. Any improved prediction of aircraft ground times can help to reduce local delays and delay propagation in the network by taking into account the forecast of future operational states for adjusted planning and delay mitigation strategies. In our work, we target to predict aircraft ground times at their stands by machine learning algorithms, where the complete turnaround sub-processes and domain knowledge are input for the models. We develop two types of models, the first type is regression-oriented that intends to forecast the exact aircraft ground time. And the second one is classification-oriented, which attempts to confirm aircraft off-block time adherence. An agent-based approach is applied to generate some synthetic data, besides, we also obtain an actual aircraft ground handling dataset from a certain European airport to validate our models. Finally, the interpretable method for the machine learning models is used to analyse the feature importances, and the feature affections on the prediction results. The results show that our classification model is capable to predict accurate aircraft off-block time adherence.

Accidents on the runway triggered the development and implementation of mitigation strategies. Therefore, the airline industry is moving toward proactive risk management, which aims to identify and predict risk precursors and to mitigate risks before accidents occur. For certain predictions machine learning techniques can be used. Although many studies have explored and applied novel machine learning techniques on different radar and A-SMGCS data, the identification and prediction of abnormal runway occupancy times and the observation of related precursors are not well developed. In our previous papers, three existing methods were introduced, lasso, multi-layer perception, and neural networks, to predict the taxi-out time on the taxiway and the time to fly and true airspeed profile on final approach. This paper presents a new machine learning method where the existing machine learning techniques are combined for predicting the abnormal runway occupancy times of unique radar data patterns. Additionally the regression tree method is used in this study to observe the key related precursors extracted from the top 10 features. Compared with existing methods, the new method no longer requires predefined criteria or domain knowledge. Tests were conducted using final approach radar data and A-SMGCS runway data consisting of 78,321 flights at Paris Charles de Gaulle airport and were benchmarked against 500,000 flights at Vienna airport.