Abstract

In this paper we present a case study of how design sketching can be used as a technique for exploring and creating a common understanding between users, designers and software developers, of the representation design requirements for supporting spatial-temporal reasoning in Air Traffic Control (ATC). The safe and expeditious control of aircraft requires the ATC controller to think in terms of 3D air space, and also plan ahead in time. We refer to this mental process as spatial-temporal reasoning. ATC is a 4D (3D plus time) problem but is currently supported by 2D tools such as the Plan Position Indicator-type radar displays that are seen in ATC centres. This requires the air traffic controllers to construct mental models of the air traffic situation to ensure safe vertical and horizontal separations between moving aircraft, and also expedite traffic flow. These objectives require prediction of traffic patterns and potential bottlenecks. To explain how we used design sketching, we report on the Task Analysis of an exemplar ATC task, and the characterisation of this task in spatial-temporal terms, and how the Ecological Interface Design principle of visualisation of constraints was applied to guide the development of the 4D visual form of the representation design. Document type: Part of book or chapter of book

Abstract

The concept of trajectory-based operations as proposed by SESAR and NextGen seeks to increase airspace efficiency and capacity by introducing time as an explicit control variable. Such form of operations lean heavily on the introduction of higher levels of automation to support the human air traffic controller in performing this new task. In previous research, and following the ecological interface design paradigm, a constraint-based decision support tool has been developed to visualize safe and unsafe fields of travel for rerouting the 4D trajectories of individual aircraft. A human-in-the-loop experiment showed that although the interface visualizes the boundaries between safe and unsafe actions, the quality of control in terms of robustness was mainly dependent on the level of expertise of the human controller. Following these findings, the goal of this study has been twofold: 1) to explicitly integrate the attributes of robust control actions into the constraint-based visualization, and 2) to enable flow-based (multi-aircraft) operations with the decision-support tool which is more in line with the mental model of expert controllers. As a result, a metric for the evaluation of robustness has been developed and three different types of structure-based control abstractions have been identified which have been integrated into the tool. Future work will focus on a human-in-the-loop experiment in order to evaluate the effectiveness of these two new additions.