Forthcoming Engage workshops (scroll down to the thematic challenge for further information):
● 12 November 2019, Madrid, Spain – TC4 Novel and more effective allocation markets in ATM
● 02 December 2019, Athens, Greece (co-located with SIDs) – TC2 Data-driven trajectory prediction
● The cybersecurity workshop, TC1 Vulnerabilities and global security of the CNS/ATM system, will follow in 2020.
The 2nd cybersecurity workshop will follow in 2020.
1st workshop: 27 March 2019, SESAR Joint Undertaking (Avenue de Cortenbergh 100, 1000 Brussels) – Bringing together the ATM community and security experts to enable a more privacy-preserving, cyber-resilient, fault-tolerant and secure ATM system. Final programme here. Download the presentations here.
CNS/ATM components (e.g., ADS-B, SWIM, datalink, Asterix) of the current and future air transport system present vulnerabilities that could be used to perform an ‘attack’. Further investigations are necessary to mitigate these vulnerabilities, moving towards a cyber-resilient system, fully characterising ATM data, its confidentiality, integrity and availability requirements. A better understanding of the safety-security trade-off is required. Additional security assessments for legacy systems are also needed to identify possible mitigating controls in order to improve cyber-resilience without having to replace and refit. Future systems security by design is essential: a new generation of systems architectures and applications should be explored to ensure confidentiality, cyber-resilience, fault tolerance, scalability, efficiency, flexibility and trust among data owners. Collaborative, security-related information exchange is essential to all actors in aviation. This is specially challenging in a multi-stakeholder, multi-system environment such as ATM, where confidentiality and trust are key.
Fuller text here (updated April 2019)
1st workshop: 06 November 2018, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain – ATM stakeholders and data scientists discussing the airspace users’ needs, methodologies and benefits of improved trajectory prediction. Final programme here. Presentations: zip file1 (9MB); zip file2 (8MB updated).
Accurate and reliable trajectory prediction (TP) is a fundamental requirement to support trajectory-based operations. Lack of advance information and the mismatch between planned and flown trajectories caused by operational uncertainties from airports, ATC interventions, and ‘hidden’ flight plan data (e.g., cost indexes, take-off weights) are important shortcomings of the present state of the art. New TP approaches, merging and analysing different sources of flight-relevant information, are expected to increase TP robustness and support a seamless transition between tools supporting ATFCM across the planning phases. The exploitation of historical data by means of machine learning, statistical signal processing and causal models could boost TP performance and enhance the TBO paradigm. Specific research domains include machine-learning techniques, the aggregation of probabilistic predictions, and the development of tools for the identification of flow-management ‘hotspots’. These could be integrated into network and trajectory planning tools, leading to enhanced TP.
Fuller text here (updated December 2018)
1st workshop: 13 November 2018, SESAR Joint Undertaking (SJU), Brussels, Belgium – Atmospheric scientists and ATM stakeholders shaping a more efficient provision and use of meteorological information in future aviation. Final programme here. Presentations: zip file1 (10MB); zip file2 (13MB).
The main objective of this challenge is to improve overall ATM system performance by providing better user-support tools based on improved meteorological (‘met’) products. The focus is on the synergy of several methods and techniques in order to better meet the needs of operational users and to support aviation safety (e.g., through creating early warning systems) and regulation-makers (e.g., moving from text-based to graphical information provision). All stakeholders may benefit from this synergy: ANSPs (e.g., sector reconfiguration and separation provision), airlines (e.g., storm avoidance), airport operators (e.g., airport management under disruptive events), and the Network Manager (e.g., demand-capacity balancing). The challenge is, therefore, to bring the following perspectives closer: (a) for meteorological/atmospheric science, the development of products tailored to ATM stakeholders’ needs, which are unambiguous and easy to interpret; (b) for stakeholders, the identification of the most suitable information available and its integration into planning and decision-making processes.
Fuller text here (updated November 2018)
The 2nd Novel and more effective allocation markets in ATM workshop was held on 12 November 2019, Madrid, Spain. Final programme. Presentations to follow.
1st workshop: 25 October 2018, University of Westminster (UoW), London, UK – A range of speakers from the behavioural sciences, economics, and ATM, debating approaches to improved modelling and methods in this new interdisciplinary area. Final programme here. Presentations: zip file (13MB).
This research explores the design of new allocation markets in ATM, taking into account real stakeholder behaviours. It focuses on designs such as auctions and ‘smart’ contracts for slot and trajectory allocations. It seeks to better predict the actual behaviour of stakeholders, compared with behaviours predicted by normative models, taking into account that decisions are often made in the context of uncertainty. Which mechanisms are more robust against behavioural biases and likely to reach stable and efficient solutions, equitably building on existing SESAR practices? The research will address better modelling and measurement of these effects in ATM, taking account of ‘irrational’ agents such as airline ‘cultures’. A key objective is to contribute to the development of improved tools to better manage the allocation of resources such as slots and trajectories, and incentivising behaviour that benefits the network – for example by investigating the potential of centralised markets and ‘smart’ contract enablers.
Fuller text here (updated November 2018)