Raphael Monstein

This study investigates inefficiencies within the Route Availability Document (RAD) used in European air traffic management, focusing on flights from Zurich Airport (LSZH) to various airports in London (EGLL, EGKK, and EGLC). The RAD imposes complex restrictions that impact flight planning, which can lead to inefficiencies. By analyzing flight plans and historical aircraft trajectories, this study highlights discrepancies between filed flight plans and actual flight paths for these city pairs, with only between 2.7% and 4.5% of the flights flying as filed. The impact of EUROCONTROL’s All Together Now 2024 initiative was also assessed, revealing no improvements in adherence to the flight plans. Statistical tests were employed to quantify differences in flight distance and duration, demonstrating that deviations from the RAD constraints lead to shorter and more efficient flights. The study proposes methods for scaling this analysis to uncover inefficiencies and suggests potential RAD modifications to enhance air traffic management efficiency.

Accurate pre-flight fuel planning is essential to ensure that an aircraft carries enough fuel for a safe flight, while avoiding unnecessary weight that reduces efficiency. However, at certain airports, frequent shortcuts on arrival can lead to systematic discrepancies between the nominal STAR (Standard Terminal Arrival Route) distances used for fuel planning and the actual distances flown. This, in turn, can result in aircraft carrying unnecessary excess fuel. To address this issue, some airports have begun to publish expected STAR distances for specific procedures in the Aeronautical Information Publication, allowing operators to plan fuel more accurately. This paper examines the impact of providing expected STAR distances by analysing one year of ADS-B data from Geneva, Munich and Rome Fiumicino airports. The study compares the observed distances flown with the full and expected STAR distances and presents econometric models to identify factors influencing the actual flown distances. In addition, fuel calculations are presented to estimate the potential benefits of publishing expected distances at airports that do not currently provide this information. The results show that for most of the STARs analysed, significant differences between observed flight distances and full STAR distances exist. However, these discrepancies are mitigated by the availability of published expected distances at Munich and Rome. The econometric model highlights consistent influencing factors such as STAR shape, shortcut potential, peak traffic hours, and weather conditions, although some effects are more location-specific. The fuel savings analysis suggests that adopting this practice at more airports could significantly reduce unnecessary fuel burn and associated emissions. Overall, this paper increases the understanding of how publishing expected STAR distances can improve fuel planning accuracy, operational efficiency, and environmental sustainability.

The analysis and prediction of go-arounds, also referred to as missed approaches, is an active field of research due to the go-around’s impact on safety and the disruption of the traffic flow at airports. The advent of open-source aircraft trajectories available to researchers has increased the level of interest in the field. This paper introduces a publicly available dataset containing metadata of almost 9 million landings and 33,000 go-arounds. The dataset is based on observations from the OpenSky Network and includes data from 176 airports in 44 countries observed in the year 2019. After downloading the data, a go-around classification was performed and the quality was assessed. The usefulness of the dataset is illustrated with two novel example applications. The first example shows how the go-around rate for a runway can be modeled by using a quasi-binomial generalized linear model, while the second example compares the go-around rates for a number of airport–airline pairs. The introduced dataset is significantly larger than the data used so far in the analysis of go-arounds and provides the opportunity to develop novel use cases. This dataset frees researchers from having to collect and process large amounts of data and instead lets them focus on the analysis. The authors are convinced that this large dataset will stoke the creativity of the research community and facilitate interesting and novel applications.

Aircraft trajectory generation is a high stakes problem with a wide scope of applications, including collision risk estimation, capacity management and airspace design. Most generation methods focus on optimizing a criterion under constraints to find an optimal path, or on predicting aircraft trajectories. Nevertheless, little in the way of contribution has been made in the field of the artificial generation of random sets of trajectories. This work proposes a new approach to model two-dimensional flows in order to build realistic artificial flight paths. The method has the advantage of being highly intuitive and explainable. Experiments were conducted on go-arounds at Zurich Airport, and the quality of the generated trajectories was evaluated with respect their shape and statistical distribution. The last part of the study explores strategies to extend the work to non-regularly shaped trajectories.

Go-arounds (GAs) are standard air traffic control procedures during which aircraft approach a runway but do not land. The incidence of a GA can subsequently affect the workload of flight crews and air traffic controllers, and might impact an airport runway’s throughput capacity. In this study, two different modeling methods for predicting the occurrence of GAs based on open-source Automatic Dependent Surveillance–Broadcast (ADS-B) and meteorological data are presented. A macroscopic model quantifies the probability of a GA within the next hour for an airport by applying a generalized additive model. A microscopic model employs a number of machine learning classifiers on trajectories of aircraft on approach in order to predict if a GA will be performed. Even though the results of the macroscopic model are promising, the information currently available to predict the probability of a GA is not detailed enough to achieve satisfactory predictions. Similarly, the microscopic model is capable of predicting 50% of all GAs, with false positive rate below 7%. Despite the limitations of the quality of the results, the authors are convinced that both modeling methods can be inspiring to other researchers and provide useful insights into the airport system under scrutiny.

In this paper, we present an aircraft localization solution developed in the context of the Aircraft Localization Competition and applied to the OpenSky Network real-world ADS-B data. The developed solution is based on a combination of machine learning and multilateration using data provided by time synchronized ground receivers. A gradient boosting regression technique is used to obtain an estimate of the geometric altitude of the aircraft, as well as a first guess of the 2D aircraft position. Then, a triplet-wise and an all-in-view multilateration technique are implemented to obtain an accurate estimate of the aircraft latitude and longitude. A sensitivity analysis of the accuracy as a function of the number of receivers is conducted and used to optimize the proposed solution. The obtained predictions have an accuracy below 25 m for the 2D root mean squared error and below 35 m for the geometric altitude.