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The current airspace route system consists mainly of pre-defined routes with a low number of intersections to facilitate air traffic controllers to oversee the traffic. Our aim is a method to create an artificial and reliable route network based on planned or as-flown trajectories. The application possibilities of the resulting network are manifold, reaching from the assessment of new air traffic management (ATM) strategies or historical data to a basis for simulation systems. Trajectories are defined as sequences of common points at intersections with other trajectories. All common points of a traffic sample are clustered, and, after further optimization, the cluster centers are used as nodes in the new main-flow network. To build almost-realistic flight trajectories based on this network, additional parameters such as speed and altitude are added to the nodes and the possibility to take detours into account to avoid congested areas is introduced. As optimization criteria, the trajectory length and the structural complexity of the main-flow system are used. Based on these criteria, we develop a new cost function for the optimization process. In addition, we show how different traffic situations are covered by the network. To illustrate the capabilities of our approach, traffic is exemplarily divided into separate classes and class-dependent parameters are assigned. Applied to two real traffic scenarios, the approach was able to emulate the underlying route systems with a difference in median trajectory length of 0.2%, resp. 0.5% compared to the original routes.
Ingrid Gerdes; Annette Temme. Traffic Network Identification Using Trajectory Intersection Clustering. Aerospace 2020, 7, 175 .
AMA StyleIngrid Gerdes, Annette Temme. Traffic Network Identification Using Trajectory Intersection Clustering. Aerospace. 2020; 7 (12):175.
Chicago/Turabian StyleIngrid Gerdes; Annette Temme. 2020. "Traffic Network Identification Using Trajectory Intersection Clustering." Aerospace 7, no. 12: 175.
At present, en-route flight traffic is carried out on a system of predefined routes with a low number of intersections between aircraft trajectories. This enables the air traffic controllers to control and supervise the traffic, especially around these intersections. Consequently, the route system leads to a low ratio of used to unused airspace, where not necessarily the shortest route is used for each flight. To reduce trajectory length, the idea of free routing has been developed, whereby each aircraft uses the direct connection between origin and destination airport, generating a traffic distribution which uses nearly the entire available airspace. As a consequence, many intersections between flight trajectories occur, making it more difficult for controllers to handle. We use these intersections as the basis of a so-called main-flow system with trajectories consisting of intersection points instead of waypoints. The intersections of all trajectories of a traffic sample are clustered and the resulting cluster centres are used as nodes in a route system. Additional processing is applied to identify a system of main flows and reduce the number of intersections to an acceptable amount. Our approach is able to identify major traffic flows within unstructured great-circle traffic and to create a main-flow system which is a compromise between the flexibility of free routing and the easier surveillance by controllers in the case of a predefined route network. To prove the ability of the proposed method to identify main flows, it was applied to a scenario of planned flights following the standard route structure. Subsequent tests with two different free-routing scenarios led to new route systems where the median adapted trajectory length for flights of the traffic sample is merely 0.9% (respectively 4.1%) higher than the direct connections. Furthermore, structural complexity is lower for intersections (cluster centres) of the new main-flow system compared to those of direct or great-circle scenarios.
Ingrid Gerdes; Annette Temme; Michael Schultz. From free-route air traffic to an adapted dynamic main-flow system. Transportation Research Part C: Emerging Technologies 2020, 115, 102633 .
AMA StyleIngrid Gerdes, Annette Temme, Michael Schultz. From free-route air traffic to an adapted dynamic main-flow system. Transportation Research Part C: Emerging Technologies. 2020; 115 ():102633.
Chicago/Turabian StyleIngrid Gerdes; Annette Temme; Michael Schultz. 2020. "From free-route air traffic to an adapted dynamic main-flow system." Transportation Research Part C: Emerging Technologies 115, no. : 102633.
Today’s air traffic operations follow the paradigm of ‘flow follows structure’, which already limits the operational efficiency and punctuality of current air traffic movements. Therefore, we introduce the dynamic airspace sectorisation and consequently change this paradigm to the more appropriate approach of ‘structure follows flow’. The dynamic airspace sectorisation allows an efficient allocation of scarce resources considering operational, economic and ecological constraints in both nominal and variable air traffic conditions. Our approach clusters traffic patterns and uses evolutionary algorithms for optimisation of the airspace, focusing on high capacity utilisation through flexible use of airspace, appropriate distribution of task load for air traffic controllers and fast adaptation to changed operational constraints. We thereby offer a solution for handling non-convex airspace boundaries and provide a proof of concept using current operational airspace structures and enabling a flight-centric air traffic management. We are confident that our developed dynamic airspace sectorisation significantly contributes to the challenges of future airspace by providing appropriate structures for future 4D aircraft trajectories taking into account various operational aspects of air traffic such as temporally restricted areas, limited capacities, zones of convective weather or urban air mobility. Dynamic sectorisation is a key enabling technology in the achievement of the ambitious goals of Single European Sky and Flightpath 2050 through a reduction in coordination efforts, efficient resource allocation, reduced aircraft emissions, fewer detours, and minimisation of air traffic delays.
Ingrid Gerdes; Annette Temme; Michael Schultz. Dynamic airspace sectorisation for flight-centric operations. Transportation Research Part C: Emerging Technologies 2018, 95, 460 -480.
AMA StyleIngrid Gerdes, Annette Temme, Michael Schultz. Dynamic airspace sectorisation for flight-centric operations. Transportation Research Part C: Emerging Technologies. 2018; 95 ():460-480.
Chicago/Turabian StyleIngrid Gerdes; Annette Temme; Michael Schultz. 2018. "Dynamic airspace sectorisation for flight-centric operations." Transportation Research Part C: Emerging Technologies 95, no. : 460-480.
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.
Annette Temme; Ingrid Gerdes; Roland Winkler. Computational Intelligence in Air Traffic Management. Artificial Intelligence: Foundations, Theory, and Algorithms 2013, 445, 285 -297.
AMA StyleAnnette Temme, Ingrid Gerdes, Roland Winkler. Computational Intelligence in Air Traffic Management. Artificial Intelligence: Foundations, Theory, and Algorithms. 2013; 445 ():285-297.
Chicago/Turabian StyleAnnette Temme; Ingrid Gerdes; Roland Winkler. 2013. "Computational Intelligence in Air Traffic Management." Artificial Intelligence: Foundations, Theory, and Algorithms 445, no. : 285-297.