Thomas Kunze’s research while affiliated with TU Dresden and other places

For most flight phases automated and reliable target time predictions for an efficient resource management are common, but during the turnaround on ground best guessing by staff is still the standard. The turnaround prediction concept of TU-Dresden, called GMAN, is an approach to predict the Total Turnaround Time and the appropriate Target Off Block Time. The proof of concept in a real airport environment shows it ability to work reliable in an automated ATM-system, with suitable adjustments to the local information environement. Further an approach with microscopic process definition to offer control options is shown.

This paper describes the major results of the UTOPIA project. UTOPIA is part of the SESAR Work Package E program, which is addressing long-term and innovative research for the Single European Sky. One of the greatest challenges that the future ATM system will need to face in the next decades is the integration of new airspace users and the continuous increase in delegating capacity and safety critical traffic management functions to automated systems. The accommodation of these new airspace users, which will have to coexist with conventional users, a widely reorganized airspace and the increased level of automation will necessarily need a paradigm shift with regard to the trajectory management functions. The objective of the UTOPIA project was to provide a better understanding of essential trajectory management functions to efficiently manage heterogeneous traffic considering the increasing presence of autonomous ATM systems. In particular, UTOPIA focused on data models, synchronization requirements and algorithms needed to ensure the safe management of merging traffic in an extended terminal maneuvering area, executed by an autonomous arrival management function acting as separator. The converging flows of traffic that was studied comprise heterogeneous airborne systems, in particular, advanced and legacy flight management systems, representing airspace users with different synchronization capabilities.

While the current turnaround handling shows potential for prediction and reliability improvement, the turnaround management approach (GMAN) of the Department of Air Traffic Technology and Logistics at TU Dresden describes a scientific foundation using a stochastic approach for process description and delay modeling. Based on recent air traffic network and delay analysis, new delay input data could be derived for Euro-pean airports. In a first step to integrate open and closed-loop process control for higher automation levels in turnaround management , the sub-processes of aircraft cleaning and boarding have been modeled and implemented, showing great potential of minimizing aircraft ground time in case of disturbances. Further enhancements to the turnaround model include the integration of the processes pushback and deicing, which by definition are not a part of the turnaround, but can significantly contribute to aircraft delay and therefore need to be considered for airport ground operations.

Due to the fact that the boarding is always on the critical path of the aircraft turnaround, efficient boarding strategies are an essential for a reliable turnaround progress. Since the boarding time mainly depends on the amount of passengers, arrival rate, passenger boarding sequence and aircraft type we investigate different boarding scenarios on three reference aircraft: Airbus A320 (single aisle), Boeing B777 and Airbus A380 (both with a twin aisle configuration). The proposed microscopic approach of modeling the passenger behavior is primarily based on the asymmetric simple exclusion process, where the passenger motion is defined as a one dimensional, stochastic, and time/space discrete transition process. The provided analysis focuses on substantial boarding strategies and the scenarios are evaluated with common statistical criteria (e.g. expected value, variance, quantiles). In the context of both reliable boarding progress and delay compensation during the turnaround our results basically emphasize the use of an additional door for the boarding process (20 - 25 % savings), followed by a change of the boarding strategy (10 - 15 % savings), and the potential application of different seat layouts (3 % savings). First validation checks are performed against measurements of field trials with Airberlin. These tests point out the high reliability of the proposed stochastic aircraft boarding model.

While the current turnaround handling shows potential for prediction and reliability improvement, the turnaround management approach of the Department of Air Traffic Technology and Logistics at TU Dresden describes a scientific foundation using a stochastic approach for process description and delay modelling. Based on recent air traffic network and delay analysis, new delay input data could be derived for European airports. In a first step to integrate open and closed-loop process control for higher automation levels in turnaround management, the sub-processes of aircraft cleaning and boarding have been modelled and implemented, showing great potential to minimize aircraft ground time in case of disturbances.

The introduction of trajectory based operation in future Air Traffic Management Systems is driven by the European SESAR and American NextGen Initiatives and relies on a common view for four-dimensional trajectories. Herein, Decision Support Tools will play an important role as automation will certainly increases over time to allow predicting and amending current 3D trajectories in a real-time environment by taking into account external disturbances to the aircraft such as weather or wind. This paper presents a model to generate 4D trajectories based on stochastic disturbances acting on the aircraft in flight. It was developed within the UTOPIA Project, part of SESAR Work Package E. Definitions for momentary position uncertainty and its projection over time as "corridor of uncertainty" are presented in this paper paving the way for a model transferring external disturbances into position uncertainty adhering to the trajectory. Results from the implemented model show quantified position uncertainties along the corridor of uncertainty based on the Eurocontrol BADA 3 aircraft performance model. The research shows that the presented approach to transfer stochastic disturbances into position uncertainty is suitable for dedicated applications but not in general.

Alle Prozesse und Koordinationserfordernisse zur Abfertigung eines LFZ ab dem Erreichen der Parkposition (In-Block) bis zum Verlassen dieser (Off-Block) werden als Turnaround bezeichnet. Die Prozesse des Turnarounds umfassen dabei im Kern das De-boarding, Fuelling, Catering, Cleaning, Loading und Boarding. Diese können aus rechtlichen und verfahrenstechnischen Gründen nur zum Teil parallelisiert werden. Kritisch ist also jene Kopplung von sequenziellen Prozessen zu werten, die bei einer Verzögerung in einem Prozess die Dauer des Turnarounds direkt beeinflussen. Der Turnaround ist im Rahmen des A-CDM zeitlich determiniert, so kann die Zielzeit für das Ende des Turnarounds (Target Off-Block-Time TOBT) bereits vor Erreichen der Parkposition festgelegt werden. Durch die Berücksichtigung der TOBT innerhalb der Verkehrsflusssteuerung und durch eine gezielt angepasste Reihenfolge des Ausparkens (Pushback) wird die Kapazität des Flughafens besser ausgenutzt. Das A-CDM Konzept lässt allerdings offen, wie und wann die TOBT konkret zu bestimmen ist. Auch existiert bisher kein standardisierter Algorithmus, um die Abfertigungsdauer eines Luftfahrzeuges mit einer vorzugebenden Mindestqualität und Zuverlässigkeit unter Berücksichtigung der Bedarfsplanung von Personal und technischen Ressourcen zu ermitteln. Im heutigen Bodenprozessmanagement werden hierfür nur feste Prozesszeiten in Abhängigkeit der Art des Flughafens (Hub versus Zubringer) und des Luftfahrzeugtyps unterstellt, ohne dabei die systemimmanenten Streuungen in der Prozessrealisierung abzubilden.

One of the greatest challenges that the future ATM system will need to face in the next decades is the integration of new airspace users and the continuous increase in delegating capacity and safety critical traffic management functions to automated systems. The accommodation of these new airspace users, which will have to coexist with conventional users, a widely reorganized airspace and the increased level of automation will necessarily need a paradigm shift with regard to the trajectory management functions. The objective of the UTOPIA project is to provide a better understanding of essential trajectory management functions to efficiently manage heterogeneous traffic considering the increasing presence of autonomous ATM systems. In particular, we will focus on the data models, synchronization requirements and algorithms needed to ensure the safe management of merging traffic in an extended TMA, executed by an autonomous arrival management function acting as separator. The converging flows of traffic that will be studied comprise heterogeneous airborne systems, in particular, advanced and legacy flight management systems, representing airspace users with different synchronization capabilities.

The A-CDM's (Airport Collaborative Decision Making) goal of accurate turnaround time prediction in not met by traditional deterministic models or best guessing. During this research, the influence of the stochastic arrival process on the turnaround process is captured by modeling stochastically all elements of the turnaround as part of a Ground Manager (GMAN). Since arrival delay is one major factor influencing turnaround time process duration and variance, and delay occurs in larger amplitudes at U.S. airports compared to European ones due the absence of slot control, the presented GMAN concept is tested with arrival delay data from the U.S. NAS (National Airspace System). The arrival delay is collected from ASPM (Aviation System Performance Metrics) and custom probability distributions were fitted on the data for different airport categories. The results obtained from this analysis by showing the proof of concept of the GMAN, are discussed in this paper Turnaround, prediction, processes, buffers, delay, A-CDM

The aircraft turnaround is a time-critical process linking flight legs with various potential disruptions far beyond those appearing during flight. This is often caused due to the lack of substantial automation and limited standardization in aircraft ground handling ranging from human resource skills to equipment types. Whenever a disturbance occurs (e.g. while boarding or fueling) as part of the so-called critical path, these effects immediately cause a disruption propagation resulting in accumulating delay through the whole air traffic network. To allow for an efficient process control and prediction, the turnaround management will have to be systematically standardized ensuring the compatibility to the expected increase of the automation level. Our proposed turnaround model is using closed-loop stochastic dynamic process optimization considering input and state constraints to bridging the efficiency gap between ground and airborne operations. It will also use stochastic models to describe every turnaround sub-process, to be shaped according to expected behavior resulting from increased automation which is based on previous research results.