A participatory design for the visualization of airspace configuration forecasts

Abstract : Currently, airspace-related activities in Air Traffic Control Centers (ATCC) are dispatched between the Flow Management Position (FMP) operators and the control room manager, and take place in two different time frames. The first activity (FMP) is the planning, 2 days ahead, of airspace usage and anticipated overloads, using coarse-grain and relatively inaccurate workload prediction metrics. The second activity (control room manager) is the day-to-day operation, where workload is re-assessed in real-time and where airspace may be re-configured according to the actual traffic of the day. In previous works, a workload model relying on relevant air traffic complexity metrics was proposed, using a neural network trained on past sector operations. This workload prediction model was combined with tree search algorithms, in order to compute optimal partitions of the airspace in Air Traffic Control (ATC) sectors. This method provides more accurate airspace configuration forecasts than today, thus improving the overall predictability of the Air Traffic Management (ATM)/ATC system. When relying on accurate 4D-trajectory predictions, as expected in the SESAR program, it could contribute towards bridging the current gap between the pre-tactical airspace/flow management and real-time operations. In this paper, we detail the participatory design approach that we used to develop a research prototype displaying the algorithm's results. As there is no such forecasting tool today, the main issue was to create a user interface in the absence of an existing user.
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Nicolas Saporito, Christophe Hurter, David Gianazza, Geraldine Beboux. A participatory design for the visualization of airspace configuration forecasts. ICRAT 2010, 4th International Conference on Research in Air Transportation, Jun 2010, Budapest, Hungary. pp xxxx. ⟨hal-00938480⟩

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