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Demand and Capacity Optimisation in U-space


Separation management process definition

Definition of procedural and tactical separation schemes and description of appropriate measures and rule based regulation schemes needed to adapt special traffic separation areas.

Structures and Rules in Capacity Constrained (urban) Environments

Description of an environment adapted set of uniform, risk based, mission aware airspace structures and rules. All rules, structures and measures need to be compliant with the boundary conditions found in complex aeronautical environments, such as urban areas with a dense population or high traffic volumes. The deliverable also includes the identification of theses potential limiting conditions.

Scenarios for validation experiments

A set of operational scenarios which will allow the team to perform a series of validation experiments aimed at testing the suitability and performance of the various prototype algorithms under nominal and sub-nominal operating conditions, as well as to support the analysis of separation intelligence balance and refinement of CNS requirements linked to separation minima criteria.

DCM Services architecture & prototype

Innovative data driven decision making algorithms (including but not limited to Machine Learning algorithms) and the associated architecture. Development of a framework that secures and optimizes the flow demand for urban drone traffic in balance with the capacity of U-Space. The base for the dynamic capacity management process are the influence factors defined in WP1 and the outcomes defined for the dynamic input models in WP3. First delivery (T12) includes a software framework ready for testing in FTS; final delivery (T22) addresses algorithm updates with FTS results.

Drone trajectory management framework prototypes

Development and evolution of services for mission planning management, flight planning management and weather in line with drone DCB concept, structures and rules in capacity constrained environments and separation management process defined. First delivery (T12) includes services ready for testing in FTS; final delivery (T22) addresses algorithms updates with FTS results.

Models and enabling technologies in support of DCB description

Mathematical model to identify the potential collision risk (for every pair of drones, and global one) considering the traffic mix and drones features, the real performances provided by the CNS and Computing systems, the population density, etc. It addresses risk and societal impact. First delivery (T7) includes outputs format and content defined; second delivery: (T10) completes conceptual models and algorithms.

AI Demand Prediction Model

Model for forecast the real demand considering operational models and characterization expressed in the WP2 for each type of drone introducing a variable coefficient impacted by micro-weather event. First delivery (T7) includes outputs format and content defined; second delivery (T10) addresses the conceptual model and algorithm.

Prototype of services to support large number of simultaneous operations

Development and evolution of services for strategic and adaptive tactical de-confliction, considering collision risk models based on the navigation, communications and tracking performances as well as drone traffic density. First delivery (T12) includes services ready for testing in FTS; final delivery (T22) addresses algorithms updates with FTS results.


Towards a continuous Demand and Capacity Balancing process for U-space.An innovative approach to implement dynamic separation criteria in Tactical Conflict Resolution and Dynamic Capacity management services as part of the U-space ecosystem

Author(s): Pablo Sánchez Escalonilla, Dominik Janisch, ATM R&D Reference Center, CRIDA. Chris Forster, Altitude Angel Reading. Michael Büddefeld, Hugo Eduardo Teomitzi, Technische Universität Darmstadt.
Published in: SESAR Innovation Days 2020, Issue yearly, 2020