Bernd Oreschko’s scientific contributions

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.

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.

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.

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.

Insufficient ground operation performance with excessive process durations is one major contributor for flight delays caused on ground. Many European airports implemented first A-CDM but still need an automated ground process understanding and prediction system. Catching up our previous research activities we focused on explanations for turnaround process variations caused by staff skills. Therefore we analyzed the skills and the training of the ground handling staff in several steps, including a document analysis and interviews of different Ground Handlin companies within Germany. The analysis of public documents showed up a huge gap in the obligatory skills for turnaround staff. A slope of professional trained staff from airports with high traffic numbers to lower ones is also apparent. Additional we will show new findings in our research of delay impacts to process variations. As an outlook, all our efforts will approach in a complex Turnaround model predicting process duration for a satisfactory determination of the Turnaround Time and TOBT.

One contributor for excessive flight delays is insufficient ground operation performance with excessive process durations. Further productivity is measured not only by the airline but also by the airport operator and the ground handling companies. Collaborating in the A-CDM with other partners, the aim is to understand process characteristics and to predict process duration for the exact Gate Occupancy Time needed for operational capacity planning. Within this paper focus is set on process variations within specific sub processes for different airports, depending on their network function. An introduction of an intermediate airport category beside hub and non-hub airport is given. We found significant dependencies of turnaround characteristics depending on airport classification, especially for loading process on hub airports.

We report on a specific calibration for an individual-based simulation environment. For this purpose field data of travelling people inside an airport terminal was recorded. The advantage of using video surveillance system is the granting unbi-ased gathering of person behavior. The presented results are derived from an observed area of 10 x 30 m area between the check-in and the security control. By means of statistical analyses a significant difference in behavior between business and leisure related passenger groups was resolved. The influence of carry-on baggage is very small and trolley bags do not affect the maximum speed of passengers. In contrast, the size of a passenger group has a significant influence on walking speed, whereas large groups tend to diverge into smaller groups with 2-3 members.

We report on a specific calibration for an individual-based simulation environment. For this purpose field data of travelling people inside an airport terminal was recorded. The advantage of using video surveillance system is the granting unbiased gathering of person behavior. The presented results are derived from an observed area of 10 × 30 m area between the check-in and the security control. By means of statistical analyses a significant difference in behavior between business and leisure related passenger groups was resolved. The influence of carry-on baggage is very small and trolley bags do not affect the maximum speed of passengers. In contrast, the size of a passenger group has a significant influence on walking speed, whereas large groups tend to diverge into smaller groups with 2-3 members.