Journal of Aerospace Operations

In order to improve air traffic management service delivery, we need to better characterise and measure performance, through improved metrics. We introduce complexity science and illustrate examples of the additional metrics it may bring to air traffic management. We show how exploring metric variability is preferred to focusing on central tendency. The importance of embracing passenger-centricity is demonstrated. The first results of applying complexity science techniques to the characterisation of actual European passenger trip itineraries are presented, investigating network topologies and vulnerabilities. It is anticipated that on-going work will further contribute to existing research demonstrating the differences between flight-centric and passenger-centric metrics, establishing the better alignment of the latter with key high-level policies in Europe. The specific metric contributions from complexity science remain to be fully proven – although the initial evidence is encouraging.

An improvement is required in the meteorological support for the future Air Traffic Management (ATM) System. New concepts of weather services must be tailored to enable a safer and more efficient aviation: better accuracy, increased data availability, real time support, digital service and shared information are some of the foundational elements underlying trajectory optimization, automation of operations, and fuel & time cost-reductions. A Digital Meteorological Service (DMET) is cornerstone in a net-centric service-oriented ATM system architecture where available data, air-ground connectivity and modern computational resources are taken advantage of to attain a 4D predictive model specifically designed for real-time support to aircraft operations. The effort presented here consists on the development of a prototype DMET service that computes atmospheric data from several sources to produce predicted 4D atmosphere scenarios regularly available to subscribers. By using many data sources –such as forecasts from global and mesoscale weather models, in-situ observations and the introduction of local airborne parameters, a well-tailored forecast product is developed. It consists of a 4D grid of pressure, temperature and wind data fields that are valid into an airspace cube of about 150 × 150 × 20 km, within a time interval of 2.5 hours. On top of this model, minimum time, minimum consumption and other interesting weather-based optimization functions are covered, all being processed in parallel for a future migration to a supercomputing centre.

It is to be expected that the task of an air traffic controller will change with the introduction of four-dimensional (space and time) trajectories for aircraft, as can be seen in ongoing developments in ATM systems in Europe (SESAR) and the US (NextGen). It is clear that higher levels of automation will need to be developed to support the management of four-dimensional trajectories, but a definite concept on a distribution of the roles of automation and human users has not yet been well defined. This paper presents one approach to the design of a shared representation for 4D trajectory management. The design is based on the Cognitive Systems Engineering framework and by using a formative approach in the analysis of the work domain, a step-wise refinement in the planning and execution of 4D trajectories is proposed. The design is described in three Abstraction Hierarchies, one for each phase in the refinement. The ultimate goal is to design a shared representation that underlies both the design of the human-machine interface and the rationale that guides the automation. It is foreseen that such a shared representation will greatly benefit the shared cognition in ATM and allows shifting back and forth across various levels of automation. A preliminary version of a joint cognitive system for 4D trajectory management has been developed and will be introduced in this paper. Further work will focus on the refinement of the shared representation by means of human-in-the-loop experiments.

This paper introduces an innovative framework for the design and implementation of new ATM decision support tools for strategic de-confliction. The main key implementation aspects to support an efficient state space analysis of more than 4000 4D Trajectories in the entire European ATM is described. The paper focuses on the innovative aspects developed to improve Spatial Data Structures, i.e. the paper focuses on the new Relational Space Data Structures and Time-Space Data Structures concepts, that allow supporting strategic Conflict Detection (CD) between a large number of 4D trajectories and a wide airspace region. Results have been tested in the WP-E project STREAM, whose aim is to coordinate the entire European ATM traffic at strategic and tactical levels, thus requiring the processing of large number of trajectories under heavy traffic conditions. The new and efficient CD algorithms presented in this paper may contribute to increase airspace capacity in the SESAR framework for the period up to 2020.

Who is responsible for accidents in highly automated systems? How do we apportion liability among the various participants in complex socio-technical organisations? How can different liability regulations at different levels (supranational, national, local) be harmonized? How do we provide for accountability, while promoting safety? These and other questions are being addressed by the ALIAS (Addressing Liability Impact of Automated Systems) project. In this paper we present the outline framework of the project, its objectives, and some preliminary results: in particular, we present a framework for liability in aviation, an analysis of real accidents and of a hypothetical case involving UAS according to a methodology developed in the project, and finally, we introduce the Legal Case, that is a methodological tool (currently under development) aimed at identifying and addressing liability issues of automated ATM systems.