We consider two very basic problems – one in unsupervisedand one in supervised learning. In the former, we are given a set ofpoints and have to label half of the points red and half the points blueso as to maximize the red–blue separation, i.e., the length of a shortestbichromatic edge. In the latter, the data (points in the plane) are alreadylabeled red and blue, and we seek a linear classifier (a separator of thetwo given point sets) that can be described using the smallest integers.We give algorithms for both problems. Our solutions are simple; themain contribution of the paper is highlighting the problems and theiralgorithmic solutions, which, to our knowledge, have not been presentedpreviously, despite the problems being fundamental to the field. We alsoconsider related problems.

Adverse weather is the primary cause of delays to air traffic. In this paper models of different maturity level from the United States, Canada and Europe are compared to derive best practices in how to mitigate these impacts. The models are illustrated through case studies in each one of these airspaces. An example in Jacksonville Center in Florida, one for Toronto Airport and one for the Rhein Airspace adjacent to Munich Airport are presented here. Lastly, some of the modeling characteristics are compared to derive best practices and lesson learned that can be leveraged from each other.

Given a simple polygon P, the minimum convex cover problem seeks to cover P with the fewest convex polygons that lie within P. The maximum hidden set problem seeks to place within P a maximum cardinality set of points no two of which see each other. We give constant factor approximation algorithms for both problems. Previously, the best approximation factor for the minimum convex cover was logarithmic; for the maximum hidden set problem, no approximation algorithm was known.

The number and diversity of space and higher-airspace missions is rapidly growing worldwide, including Europe. However, the prosperity of these new entrants may become incompatible with that of the conventional aviation, unless their coexistence is regulated. Since “what gets measured gets managed”, the impact on the conventional air traffic needs to be evaluated. To that end, we conduct a literature review and propose a methodology to quantify the impact that a real, segregated, special operation within a vertically unlimited volume had on regular air traffic. This methodology is then applied to a recent disruptive event in southern Europe.

Accurate prediction of the Air Traffic Control (ATC) sector capacity is a cornerstone in solving the demand/capacity imbalance problem in aviation. In this paper, we develop a methodology, based on the continuous maxflow/mincut theory, to estimate the reduction of the ATC sector capacity due to predicted convective weather activity. The meteorological forecast uncertainty is quantified using Ensemble Weather Forecasting. We demonstrate how to determine congestion in ATC sectors, using an example of a realistic sector, also a whole sector configuration, and propose a way to present the probabilistic overload and congestion status to support the decision-making process at the Flow Management Position.

This paper explores tradeoffs between ground impact and efficiency of drone flights in urban scenarios. We give an algorithm which produces a set of routes with different lengths and varying number of people affected by the drone. We also present an interactive online visualization tool allowing the user to modify flightpaths in order to explore routing options. Our path finder and the GUI are implemented for a metropolitan area of Norrköping municipality in Sweden. The methods studied in this paper may give UTM service provider the tools to negotiate flightplans which will be acceptable by both the regulator and the drone operator.

Specific Operations Risk Assessment (SORA) is a qualitative methodology for assessing risks of drone operations. In this paper, SORA is compared to and complemented with quantitative estimations of the risk (earlier called HFRM: High-fidelity risk modeling). We highlight intrinsic shortcomings of both SORA and HFRM, and show how HFRM may help to deal with SORA’s ambiguities. (We do not have a recipe to remedy HFRM’s drawbacks with the help of SORA, but suggest a possible regulatory fix to HFRM, addressing its deficiency.) With its focus on ground risk, this paper complements the works of TU Dresden which suggested integrating “agent simulation as air risk assessment in SORA” [Fricke et al., ATM Seminar 2021] and of SESAR’s ER4 BUBBLES project “proposing a quantitative risk analysis which enhances or replaces the qualitative model of SORA” (also for the air risk) [BUBBLES Deliverable 4.1]; we also connect to CORUS observations on SORA shortcomings and use U-space services for addressing them. Our work advocates for stricter regulations, including digitalization and automation not only in definitions, but also in mandates/requirements. Our arguments are illustrated on simple synthetic cases and on real-world experimental examples from urban areas.

We give algorithms for splitting a geographical region into ”approximately flat” rectangular zones. Each zone is assigned a feasible flight altitude, providing simple flight level guidance for future operations of unmanned aerial systems in low-level airspace. We consider a rural scenario with uneven ground level and an urban setting featuring many tall structures. In both cases, our solutions adapt to the underlying terrain or city landscape. In the rural scenario, operating on a fixed altitude within a rectangle allows the drone to stay within the upper limit of 120m while not flying too close to the ground; our objective is to minimize the complexity of the airspace. In the urban case, we aim at minimizing the volume of airspace reserved for drone operations, while allowing overflight over tall buildings in the city. Experiments with real landscape and city skyline data demonstrate output of our solutions with various input parameters.

We present a mixed-integer programming (MIP) approach to compute aircraft arrival routes in a terminal maneuvering area (TMA) that guarantee temporal separation of all aircraft arriving within a given time period, where the aircraft are flying according to the optimal continuous descent operation (CDO) speed profile with idle thrust. The arrival routes form a merge tree that satisfies several operational constraints, e.g., all merge points are spatially separated. We detail how the CDO speed profiles for different route lengths are computed. Experimental results are presented for calculation of fully automated CDO-enabled arrival routes during one hour of operation on a busy day at Stockholm TMA.