Ingrid Gerdes

Trajectory based ground movements and their coordination with departure management

Today no detailed ground trajectories exist; although trajectory based ground operations would improve the runway sequence optimization by more accurate taxi times. The aim of this study was to extend the time-based trajectory management to the ground and link it with departure management. A specific futuristic mean to achieve time-based trajectories on ground is by precisely controlling the speed of aircrafts. A concept for a surface management system is introduced, which supports air traffic controllers in this task. The interactions between this advanced surface management system and departure management are studied by building and extending surface and departure management research prototypes. Preliminary results from a workshop with air traffic controllers indicate that our concept is promising.

A Concept of Operations for Trajectory-based Taxi Operations

A harmonized concept of operations (ConOps) for future surface operations that considers the surface management practices and policies that currently exist in the U.S. and Europe was developed by NASA and DLR. The high-level concept vision is to develop the framework for surface traffic scheduling systems that generate conflict-free four-dimensional trajectories (4DTs) for all aircraft on the airport surface and for guidance means on board and on ground to enable the flight crew to adhere to the trajectories. This vision supports the reduction of temporal uncertainty and delays by improving the whole planning chain from gate to runway and vice versa. This paper identifies and describes the necessary functions of this concept and explains the relationships among them. As a result of this activity, challenges arose which are the basis to derive research requirements that are jointly approached by NASA and DLR in the future. The concept is supported through a solid base of results from research already conducted in that area to support the implementation of this concept in the future.

Performance evaluation of the approaches and algorithms using Hamburg Airport operations

The German Aerospace Center (DLR) and the National Aeronautics and Space Administration (NASA) have been independently developing and testing their own concepts and tools for airport surface traffic management. Although these concepts and tools have been tested individually for European and US airports, they have never been compared or analyzed side-by-side. This paper presents the collaborative research devoted to the evaluation and analysis of two different surface management concepts. Hamburg Airport was used as a common test bed airport for the study. First, two independent simulations using the same traffic scenario were conducted: one by the DLR team using the Controller Assistance for Departure Optimization (CADEO) and the Taxi Routing for Aircraft: Creation and Controlling (TRACC) in a real-time simulation environment, and one by the NASA team based on the Spot and Runway Departure Advisor (SARDA) in a fast-time simulation environment. A set of common performance metrics was defined. The simulation results showed that both approaches produced operational benefits in efficiency, such as reducing taxi times, while maintaining runway throughput. Both approaches generated the gate pushback schedule to meet the runway schedule, such that the runway utilization was maximized. The conflict-free taxi guidance by TRACC helped avoid taxi conflicts and reduced taxiing stops, but the taxi benefit needed be assessed together with runway throughput to analyze the overall performance objective.

Computational Intelligence in Air Traffic Management

The demand for increasing airport capacity combined with many constraints as well as the complexity of the data itself leads to the use of heuristic methods from the computational intelligence domain. More specifically, the focus in this paper is on how (fuzzy) clustering methods and evolutionary algorithms are applied on various aspects of the Air Traffic Management domain. Fuzzy clustering techniques have been used for data evaluation and pre-processing. One task is the identification and correction of noise and outliers in radar tracks as a pre-processing step. In addition, clustering has been applied to identify general flight routes in retrospective analysis tasks as well as to generate fuzzy rules, thus verifying or complementing expert knowledge regarding transfer passenger movements. Evolutionary algorithms are used to assist air- and ground traffic controllers. Namely in Rogena (free ROuting with GENetic Algorithms) for route planning and TRACC (Taxi Routes for Aircraft: Creation and Controlling) for ground movement planning. Both systems create conflict free routes for aircraft which are suggested to the air- and ground traffic controllers, respectively.