Airport and passenger management

As airports are closely interconnected with each other and with ground-based traffic, efficient management of local operations is crucial for the performance of the overall system. The smart passenger of the future expects seamless travel within the transportation system. The passengers make planning and decisions about their travel based on their own information (e.g., Google Maps). The consequent imbalance between controlling entities (e.g., airports: can control but have limited information) and passengers (own information but have no ability to influence the process) must be overcome. The actual service tasks at the airport (e.g., check-in, baggage claim) are also increasingly being performed by passengers, and it is to be expected that digitalization and automation will permanently change the current landscape of an airport.


The research area can be divided in principle into two major parts: airport terminal (landside) and airport apron (airside). The topics in the airport terminal area mainly address passenger handling processes and management, considering individual requirements. In the apron area, the focus is on all operations related to aircraft handling, the optimization of process flows during handling, concepts for automation, and the collection and processing of sensor/process data.

Terminal - Landside


Digital, virtual cabin and sensor environment

The aircraft cabin is a confined space where passengers occupy their assigned seats before takeoff and vacate them again after landing. Active control (optimization) is only limitedly possible in this environment. An essential aspect of this situation is the absence of cabin status information, as no sensors are available in the cabin. The design and implementation of a virtual cabin should serve as the foundation for a real-time situation display, the creation of new services and products, and the design of an adaptive process.


Smart passenger - communication and seamless connections

Smart and digitally connected passengers make independent decisions and manage their necessary information within the transportation system individually. The desire of various system owners to gain control over passenger data is generally unsuccessful (e.g., airports or airlines), as the state of the transport system along arrival/departure ways of the passengers is provided most effectively by large data providers (e.g., Google Maps: traffic jam notification or travel time predictions). Thus, the passengers remain invisible to the airports until shortly before they arrive at the terminal and after leaving there.  Efficient passenger handling and control of the handling facilities is therefore only possible to a limited extent.  Given this context, it is reasonable to anticipate that future airport processes will undergo substantial modifications. In our research, we are investigating the impact of independently, autonomously, and self-optimizing passengers in the transportation system.


Energy management in the airport terminal

The handling processes at the airport are flight-plan-oriented and require different use of various infrastructures (e.g., counter opening hours) depending on the operational concept and capacity utilization. The passenger follows the check-in procedures in the terminal and is guided to the departure gate via different areas. With a focus on the energy consumption in the terminal (heating, ventilation, air conditioning), the dynamic passenger processes could have a significant impact on building management efficiency. The purpose of a holistic approach to building and passenger control is to demonstrate the potential and develop efficient operation concepts.


Prediction of process sequences and times

Data-driven methods can reveal correlations between airport processes that are insufficiently understood due to their complex interactions. These interactions are thus also not part of simulation environments, which thus continue to show the potential for improvement. A separation of data-driven methods and model-based methods would not be very purposeful. On the one hand, already-known correlations do not have to be "found" first in data analyses. On the other hand, new correlations can enhance created models and lead to higher accuracy in simulated scenarios. Another aspect lies in the development of machine learning methods, which require a vast amount of training data, however, these are generally unavailable. Here, model-based simulations can be used to generate the missing data and test the principle suitability of the procedures and train the machine learning models. Thus, the models can already learn fundamental aspects and already adapted to the later application in the real environment.


New requirements

The COVID-19 pandemic demonstrated that, in addition to requirements for traffic safety (Safety) and aviation security (Security), requirements for preventing the spread of epidemics have a significant impact on airport process flows. It is expected that these requirements will also need to be permanently incorporated into the strategic planning and operation of an airport. An airport must be able to react quickly and (re-)activate the necessary processes based on the circumstances. We design new concepts for airport terminals and combine them with the research work on smart passengers and coupled energy management.


  • Airport in a Lab: modular simulation environment of airport terminals, including the integration of facility management
  • Development and operation of a virtual passenger cabin (mixed-reality).
    • Mapping of passenger interactions in a digitally connected cabin
    • Integration and testing of innovative cabin and passenger processes (location-based services)
  • Machine learning (AI) using simulated training data and conducting (partial) validation in virtual environments


Publications (selected)

A combined optimization–simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage
M Schultz, M Soolaki, M Salari, E Bakhshian (2022). Journal of Air Transport Management 106, 102258

Multipath-Assisted Radio Sensing and State Detection for the Connected Aircraft Cabin
J Ninnemann, P Schwarzbach, M Schultz, O Michler (2022). Sensors 22 (8), 2859

The Rise of the Smart Passenger I: Analysis of impact on Departing Passenger Flow in Airports
MM Mota, P Scala, M Schultz, D Lubig, M Luo, EJ Perez (2021). 11th SESAR Innovation Days

COVID-19: Passenger Boarding and Disembarkation
M Schultz, M Soolaki, E Bakhshian, M Salari, J Fuchte (2021). USA/Europe Air Traffic Management Research and Development Seminar

Apron - Airside


Resource and gate assignment

Aircraft are handled on the airport apron and all necessary resources are allocated for the required operations. Deviations from the planned processes due to delays during the course of the day require constant adjustments to the initial planning, considering a multitude of constraints (e.g. departure slots, transfer passengers, restricted parking capacity on the apron). The objective of our research is to establish an application-oriented simulation environment in which a large number of operators is considered (e.g. apron traffic, ground handlers). In this way, optimized concepts can be tested directly in an interacting, virtual environment for their implementation capability. As with the landside processes, a large amount of data is to be generated if machine learning methods are to be used. Here, we set a focus on the aircraft turnaround, which is described by individual milestones (time stamps in the process) and is to be predicted using (un-) complete data.


Automated apron and upcoming technologies

The availability of advanced technologies enables processes to be increasingly automated. The airport, as a self-contained environment with well-defined access restrictions and simple traffic rules, is an ideal environment for incremental automation. First, however, a situational awareness of the airport must be generated by an appropriate sensor infrastructure. These sensors can be of various types and (a) part of the infrastructure, (b) integrated with the vehicle fleets, and (c) installed on the aircraft. For this purpose, an application-oriented simulation environment is being enhanced with synthetic sensors and integrated into a sensor framework. Since the data basis is a simulation (the position of all elements is known), investigations on the coverage and quality of the necessary sensor feedback can be conducted. Parallel to the integration of sensors, the effects of new technologies on the handling processes (e.g. use of electric vehicle fleets) are also investigated in our research.


Virtual Reality

Workplaces and environments of operators will gradually adapt to the possibilities of new technologies. Virtual reality is a key technology for obtaining extensive information about the system to be monitored, even remotely. However, virtual reality in this context does not refer to recreating a real environment; rather, it refers to providing and highlighting relevant information and elements. However, these must be derived from the available data and transformed into operator-appropriate information. For example, planned ground trajectories of aircraft could be superimposed to highlight critical intersection areas, or information could be provided regarding the turnaround sequence of each aircraft.


Real-time control of airport processes

To manage an airport, information from various areas (airside, landside) and from different system owners (e.g. airport, airline, air traffic control, handling services) must be combined. In an integrated management approach, decisions are made jointly while taking into account the respective target functions. From the observation of aircraft movements (ADS-B data), operational concepts can be derived and simple process predictions can be made without in-depth knowledge of the respective airport. The objective of research in this area is to create a data-sparse implementation of a simplified AOCC (Airport Operation Control Center) environment, which will be contrasted with representations of the actual environment during advanced development.



  • Creation of a virtual airport (airside) and mapping with process simulations of specific apron processes.
  • Simulation of apron traffic (Anylogic) coupled with simulation of virtual sensors (BlAInder)
  • Derivation of a situation picture from sensor feedback and matching with the virtual environment to identify technological requirements (coverage: quality vs. quantity)
  • Model-based process simulation for training of data-driven AI solutions (model-supported data augmentation)
  • Merging specific sensor simulation into a common platform for application-oriented concept implementation and future technologies evaluation
  • Recording and processing of ADS-B data, real-time representation of airport operations


Publications (selected)

Data-driven airport management enabled by operational milestones derived from ADS-B messages
M Schultz, J Rosenow, X Olive (2022). Journal of Air Transport Management 99, 102164

Modeling Aircraft Departure at a Runway Using a Time-Varying Fluid Queue
E Itoh, M Mitici, M Schultz (2022). Aerospace 9 (3), 119

Synthetic Training of Neural Networks for Semantic Segmentation of LiDAR Point Clouds
M Schultz, S Reitmann, B Jung, S Alam (2022). International Workshop on ATM/CNS

Optimal Schedule Recovery for the Aircraft Gate Assignment with Constrained Resources
E Asadi, M Schultz, H Fricke (2021). Computers and Industrial Engineering 162, 107682

Agent-based simulation for aircraft stand operations to predict ground time using machine learning
M Luo, M Schultz, H Fricke, B Desart, F Herrema, RB Montes (2021). IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), 1-8

Future aircraft turnaround operations considering post-pandemic requirements
M Schultz, J Evler, E Asadi, H Preis, H Fricke, CL Wu (2020). Journal of Air Transport Management 89, 101886