This document describes the research to be undertaken by the project OptiFrame – “An Optimization Framework for Trajectory Based Operations” - funded by the EU call “SESAR 2020 Exploratory Research: First Call for Research Project”, research topic “Trajectory Based Operations (TBO)” (ER-09-2015), within the area “ATM Applications-Oriented Research”. The project consortium comprises University of Lancaster (Project Coordinator), the Consorzio Futuro in Ricerca, Eurocontrol and the Stichting Nationaal Lucht- en Ruimtevaartlaboratorium (NLR). OptiFrame is motivated by the need of studying a number of fundamental questions related to TBO, a key element of future ATM operating concepts. The main objective of this research proposal is the application of principles of mathematical modelling and optimization to optimally configure and assess the performance of the TBO concept. This will allow to verify the viability of the TBO concept, to identify the major issues that need to be addressed, and determine whether, under which conditions, and to what extent, the objectives of flexibility of airspace users and predictability of the ATM system, can be achieved. The core activity and focus of this proposal is the development of a framework, which consists of mathematical models and optimization algorithms, “to support the ATFCM decision making process” by suggesting optimal TBO solutions. The framework will be applied in real world instances, and it will be used to perform a wide array of analyses. We will use OptiFrame as a tool to: i) investigate several of the issues and questions arising for the exploitation and deployment of the TBO concept, ii) fully understand the benefits and limitations of the TBO approach, and iii) study the trade-off between different contrasting KPIs relevant for the TBO concept.

DARWIN ambition and vision is to develop technology enabling AI based level 4 automation for cockpit and flight operation as a key enabler for SPO (Single Pilot Operations) and demonstrate the same (or higher) level of safety with same (or lower) workload as operations with a full crew. It will bring solutions that will help the market maintain operational efficiency with increased complexity and routing flexibility, which are expected by the emergence of drones and air taxis. The results will support the commercial and operational viability of those new airspace users, even with the forecasted pilot shortage and growing environmental concerns. AI-based automation will come with its own challenges that need to be addressed to keep the high safety standards for the next generation of automation. One of the biggest challenges is to facilitate the cooperation between humans and AI. The DARWIN project builds upon the available technology base in AI and leverages the partners’ excellent position in the aviation supply chain to address the need for scalable, interconnected, and highly automated eMCO (Extended Minimum Crew Operations) and SPO operation concepts as one of the inherent foundational building blocks of the Digital European Sky (SESAR ATM Master Plan Phase D). The system will consist of 3 core enabling technology layers: 1) Trustworthy Machine Reasoning Platform will provide capabilities for rule-driven, transparent, and explainable decision aiding or decision making. 2) Human-AI Collaboration layer will be implemented on top of the Reasoning Platform. It will provide collaborative capabilities for the pilot interaction with the adaptive automation and assistants to efficiently keep the human-in-the-loop of the workflow in the eMCO or SPO cockpit with the Level 4 automation. 3) Pilot State and Taskload Monitor will provide data to the collaboration layer and automation to adaptively react. The project will deliver a TRL7 system validated in ops environment.

The main objective of SynthAIR is to explore and define AI-based methods for synthetic data generation in the domain of ATM system due to the limitation of AI-based tools development by the lack of enough data available (e.g., safety-related data) and the problem of generalization of those AI-based models. We want to explore data-driven methods for synthetic data generation, since they require 1) less user knowledge expertise (no need to derive the explicit model of the distribution), 2) better generalization capabilities. More in detail, inspired by recent advancement in Computer vision and Language Technology, we propose the concept of Universal Time Series Generator (UTG). A UTG, is a model trained on several different time series, and able to generate a synthetic dataset representing a new dataset, simply conditioned by a compressed representation of it. In aviation domain, this generator can be trained on a certain set of data related, for example to few airports, and be used to generate synthetic data from a new airport. The same principle can be applied to define a universal time series forecaster (UTF) able to do prediction to a new environment (I.e., data from a new airport) without any new training.

The constant growth of air traffic will put regional and local airports under ever more pressure for increasing their capacity. However, safety and efficiency gains allowed by the deployment of A-SMGCS systems currently remain out of most small to medium size platforms’ reach because of prohibitive infrastructure costs. Due to the intrinsic attributes of current surveillance technologies and airport surface coverage constraints, airports are required to deploy complex and costly infrastructures involving multiple sensors of different kinds: ADS-B, MLAT, SMR. ENVISION is an ATM application-oriented research project, under the SESAR 2020 Programme “Enabling Aviation Infrastructure: CNS” topic which aims at making use of technical progress in CCTV cameras, LIDAR technology and image processing techniques, and at taking advantage of reduced equipment costs, to provide regional and local airports safe and affordable surface movements surveillance capabilities for ATC and A-CDM services. In a stepwise approach, in two airports, the consortium will evaluate and demonstrate the operational, technical and economic feasibility of implementing CCTV and LIDAR technology in complement of ADS-B on the airport surface in providing identification and positioning data in A-SMGCS. Additionally, the project will assess the benefits of using this technical solution to feed A-CDM (Airport Collaborative Decision Making) milestones and provide a surveillance display to airport stakeholders.

Air Traffic Flow Management (ATFM) is the problem of adjusting the traffic demand in each traffic volume using ATFM measures so that aircraft can be safely separated during the subsequent Air Traffic Control (ATC) process. On the other hand, ATC officers (ATCOs) give different aircraft heading, speed, and flight level change instructions to separate them in flight. Both ATFM and ATC problems have been subject of research during decades, however, all previous works addressed the ATFM and ATC problems independently. The project aims to develop an HyperSolver based on advanced Artificial Intelligent Reinforcement Learning method with continuous reassessment and dynamic updates, i.e. an holistic solver from end-to-end, covering the whole process to manage, density of aircraft, complexity of trajectories, interactions (potential conflict in Dynamic Capacity Balancing timeframe) of trajectories, conflict of trajectories at medium-term and conflict of trajectories at short-term.