Computational Intelligence in Air Traffic Management

Abstract

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.

Original document

The different versions of the original document can be found in:

• http://www.springer.com/series/7092

• http://www.springerlink.com/index/pdf/10.1007/978-3-642-32378-2_20,

http://dx.doi.org/10.1007/978-3-642-32378-2_20

• https://elib.dlr.de/86013,

https://link.springer.com/chapter/10.1007%2F978-3-642-32378-2_20,

https://rd.springer.com/chapter/10.1007/978-3-642-32378-2_20,

https://academic.microsoft.com/#/detail/1525684826