Obrad J. Babic

Airspace daily operational sectorization by fuzzy logic

From the point of view of Air Traffic Control the airspace of a country or region are divided into sectors. Each sector is served by a team of controllers. Depending on traffic characteristics (volume, scenario, aircraft types, etc.) several sectors can be merged into one sector or work independently. When the traffic characteristics are known in advance (planned or forecasted) the problem that arises is to determine the number of open sectors during a given time period (day, week, etc.) so that the traffic requesting service can be served with an acceptable work load for the controllers. In this paper, a decision support tool based on fuzzy logic is proposed to solve the above-mentioned problem. The proposed decision support tool is illustrated by a numerical example from real life.

Air route flow management — problems and research efforts

Some elements of the air traffic system are congested at certain periods because traffic demand is higher than the capacity available. The paper considers the air traffic flow management problem (FMP) on a congested air traffic network. Emphasis is placed on the situation in Europe where congestion exists not only on the network nodes (airports) but also on the links (air routes). It is contended that congestion should be helped by flow management measures. FMP is discussed here and defined in several ways. Different assumptions are proposed as well as different metrics to be used in FMP modelling. Efforts to solve the problem are reviewed and some research directions are discussed.

Fuzzy inference approach to the flow management problem in air traffic control

In the last few years congestion has appeared in some elements of the Air Traffic Control (ATC) system (airports, airways, sectors, etc.). Congestions is caused by the reduced capacity of certain elements of the ATC system during some time periods. During congestion periods some flights are delayed. As result, carriers operating costs are increased and passengers’ level of service is decreased. In this paper a single airport congestion problem is considered for the landing operations in a multi‐period framework a model for minimization of the total cost caused by aircraft delay is developed. The developed model is based on the Fuzzy Inference Technique and is illustrated by a numerical example. Some future research in this field is proposed.

Assigning fighter plane formations to enemy aircraft using fuzzy logic

Fighter aircraft protect specific facilities on alert in the air by patrolling expectation zones. These zones are located in the direction from which enemy aircraft attacks are expected; fighter formations are sent from them to intercept enemy aircraft. The problem considered in this paper is to determine the optimum assignment of fighter plane formations to enemy formations. The proposed solution is based on fuzzy logic and integer linear programming. A numerical example is given to illustrate the application possibilities of the proposed solution.

OPTIMIZATION OF REFUELLING TRUCK FLEETS AT AN AIRPORT

This paper considers the process of aircraft refuelling using refuelling trucks at an airport. The problem which arises is to determine the minimum number of refuelling trucks and their routes for the given demand (schedule and fuel quantities). The constraint is that there is no aircraft departure delay due to the refuelling process. The optimum solution is found here using the branch-and-bound technique. This solution is compared with results obtained on the basis of random assignment and a few conclusions are made. The paper also presents a model application example based on real life data from a medium-sized airport.

OPTIMUM RUNWAY EXIT LOCATION

A model to be used to find optimum runway exit location is proposed. The objective was to minimize the cost of taxiing to the terminal area after landing in the case when aircraft pass by this area during landing. This objective is relevant for many airports as compared to the objective usually stated in runway exit location optimization, to increase runway capacity, which is relevant only under saturation conditions. The proposed model is based on the simple searching procedure which shows to be efficient enough for a realistic choice of a variable domain and the value of the discrete searching step. Model inputs are the aircraft type mix in the fleet and the unit taxiing cost and the probability of exit acceptance for each aircraft type. A numerical example of model application is presented.

HAZARD IDENTIFICATION APPROACH FOR FUTURE HIGHLY-AUTOMATED AIR TRAFFIC MANAGEMENT CONCEPTS OF OPERATION: EXPERIENCES FROM THE AUTOPACE PROJECT

In future air traffic management (ATM) a significant increase in automation is expected, in order to cope with growing air transport demand. The automation will take more active role during the provision of the air traffic control (ATC) services, while future air traffic controller (ATCo) will monitor and/or approve actions performed by automated ATC systems. ATCo will need to be trained to safely adapt to new role, with special emphasis to be prepared for active participation in the case of automated system failure (non-nominal situations). Developing appropriate training should rely on assessed safety hazards in future ATM. AUTOPACE project (funded by the SESAR Joint Undertaking within the framework S2020 Exploratory Research Programme as part of the Horizon 2020 programme) looks into 2050 and beyond. In order to assess safety hazards, an approach based on hazard identification brainstorming sessions with operational experts, combining four well known and complementary methods used in aviation is proposed. Future ATCo environment is observed through two main parts. Internal, core part contains ATCo and ATC system and their relations. External part (environment) gathers Local Traffic Manager, System Wide Information Management (SWIM), other ATC systems/ATCos and traffic (aircraft/pilot). Two expert brainstorming sessions were performed based on future tasks and description of nominal and non-nominal situations which were defined at the beginning of the project. One, with academic experts, resulted with initial set of hazards. The second, with operational experts (experienced ATCos), provided a validation of the initial set and some additional, complementary hazards. Final output from both sessions is the list of “operation specific” (general) and “task specific” hazards identified. Those hazards are subject to further characterization (assignment of severity and likelihood), aiming to determine safety critical hazards that will serve as an input for development of new training methods for future ATCos.