Annette Temme

A fuzzy rule system describing transfer passenger movements

The analysis task presented here is based on the airports’ landside. The long-term idea is to build a total airport simulation system where microscopic models are used in areas of specific interest and other regions are completed by macroscopic models based e.g. on flow models. Here, the aim is to generate rules describing the passenger flow in the terminal area that can be used for macroscopic models. Fuzzy clustering methods presented in [7,9] enable us to extract in the first step a rule system based solely on available data. The methods used here are able to handle cluster structures of different extensions and to cope with outliers in the data set. Data analysis enables us to identify influence factors that might be underestimated or overlooked by experts.The result is a rule system that gives us rules in the form “under certain conditions usually an amount of about x % passengers are transfer passengers”. Therefore, the transfer passenger rate is determined in dependence on the flight time, distance to destination, and aircraft size. Although the results represent a good description of the amount of transfer passengers in general, contradictory rules occur in this example. Incorporating expert knowledge in the rule system will be necessary to develop a reliable and consistent description of passenger movements in the terminal

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.