Aircraft Turnaround Management in a highly automated 4D flight operations environment

This blog shall outline the research activities that are conducted in the area of Aircraft Turnaround Management by Technische Universität Dresden, Germany, and which is partly funded by the HALA! Network.

1. Introduction

The turnaround (TA) marks the beginning and end of the “gate-to-gate” approach linking two flight legs of an aircraft. Since it is a time critical process with several sub-processes running sequential and in parallel, it is often a significant contributor to delays in air traffic operations.
The aim of the turnaround management is the prediction of the time an aircraft occupies the stand or gate, which is represented by its turnaround time (TTT). Through the use of a turna-round time in respective to the in block time, a Target-Off-Block-Time (TOBT) can be deter-mined for each flight, giving each aircraft a time where it is supposed to leave its stand or gate and enabling sequencing of departing flights through a departure manager (DMAN) and other decision support tools, e.g. surface management (SMAN). The Milestone concept introduced by EUROCONTROL as part of the Airport Collaborative Decision Making (A-CDM) increases the predictability of TOBT by sharing information between involved partners at an airport throughout the gate-to-gate process. However, the information on ground shared between all stakeholders when a flight is dispatched, is mainly based on the experience of the ground handling operator. Likewise, conflicts arising within a turnaround are not predictable with automated means and are manually managed by operations or ramp agents. New technologies like Radio Frequency Identification (RFID) may contribute to efficient operations and allow an earlier decision making by all stakeholders through sharing this information, but also leave the decision and strategies on how to intervene into the process to the ground handler.

2. Objective

The aim of this Ph.D. project is the development of a turnaround prediction model using stochastic distribution functions to describe turnaround process times and investigate control strategies to replace the currently used “best-guess”-method. Control theory shall be used to find the optimal place and time for an intervention in case of plan deviation, as well as intelligent prediction and correction strategies for those processes promising the least cost and best outcome for all stakeholders. In the end, a proof of concept simulation environment shall be created to show the benefits of the proposed model and control strategies.

3. Methodology

Coming soon.

4. Expected Results

Coming soon.

5. References

Coming soon.