In this paper an airspace sectorization framework for terminal maneuvering areas based on mixed integer programming is presented. It incorporates an airspace complexity representation, as well as various constraints on the sectors’ geometry, for example, the requirement that points demanding increased attention from air traffic controllers should lie in the sector’s interior to allow for enough time to resolve possible conflicts. The method can enforce convex sectors. In contrast to earlier integer/constraint programming approaches, which used synthesis methods with variables per elementary airspace piece that were glued together to form sectors, the integer programming formulation uses a variable per potential edge on the sector boundary. It is also the first step toward an integrated design of routes, the resulting complexity, and a sectorization. This paper presents results for Stockholm Arlanda airport and compares the integer programming results to convex sectorizations obtained by enumerating all possible topologies for a given number of sectors. This yields a proof-of-concept for the application of this highly flexible approach to terminal maneuvering areas.

This paper studies strategic conflict resolution for air traffic based on sum coloring. We consider two application scenarios: manned and unmanned air traffic, with similar targets: to improve efficiency of operations and to reduce the costs. For the Unmanned Air Vehicles Traffic Management (UTM) we consider also a payment mechanism which incentivizes the operators to share information necessary to find a socially optimal solution. We quantify the potential savings via a series of experiments, showing that our methods drastically outperform the widely used FirstCome-First-Serve (FCFS) strategy.

In this paper, we introduce a Dantzig-Wolfe reformulation to compute aircraft arrival routes in a terminal maneuvering area (TMA) where the aircraft are flying according to theoptimal continuous-descent-operation (CDO) speed profile with idle thrust. This model assumes fixed entry times for all aircraft and a single separation time that is independent of wake-turbulence categories.Preliminary experiments show that this approach leads to significantly reduced runtimes.Moreover, the results indicate the possiblity of extending the reformulation to the full model and applying it to address additional practical considerations, such as wind effects in the future.

We present an optimization framework for computing STARs that slowly change over time, while always avoiding a set of moving obstacles in TMA. The framework is applied to two types of obstacles: a drone intruder and hazardous weather. We demonstrate the output of our algorithms on synthesized drone intrusion incidents and real storm cells in Stockholm Arlanda terminal area. 

The paper focuses on the performance assessment of the arrival operations in Oslo Gardermoen airport implementing point merge (PM) procedures. We take a data-driven approach based on the open-source ADS-B data, and conduct a detailed performance assessment utilizing a diverse set of performance indicators, including newly developed metrics for better understanding of the PM specifics. The results of the performance evaluation indicate that the PM systems are currently underutilized in Oslo airport, and their increased usage may lead to the improved arrival performance, especially during the peak time periods.

We present an application of a mixed-integer programming (MIP) framework for automatic traffic synchronization, providing safe separation between the arriving traffic within the terminal maneuvering area (TMA) of an airport implementing point merge (PM) procedures. Additionally, the proposed methodology ensures conflict-free operations when departures and arrivals share a common runway. Based on real traffic scenarios for two European airports, we model realistic descent profiles and assume all the arrivals are performing the most fuel-efficient continuous descent operations (CDOs). We compare two scenarios: in the first, the arriving aircraft are strictly forced to adhere to the published arrival route structures, meaning that a turn towards the merge point may not be initiated prior to reaching the point merge system (PMS), while in the second scenario, aircraft may be assigned a shortcut from a published waypoint along the arrival route. We evaluate the resulting arrival flight efficiency and compare it to that of the actual flights, arriving during the hour selected for our optimization, noticing varying benefits for the two airports and whether shortcuts are allowed or not. Given the correct setting for the specific airport, we demonstrate that our approach provides significant benefits, including increased vertical performance as well as reduced time and distance, contributing to lower levels of noise and fuel savings, accompanied by reduced emissions.

Analysis of the sequence of arriving aircraft, as well as identification of the cases of spacing violations, is an important step in evaluating performance of the Terminal Maneuvering Area (TMA) Air Navigation Services: without knowing the current performance levels, it is difficult to identify which areas could be improved. This work presents an enhanced data-driven methodology for evaluation of arrival aircraft sequencing and spacing inside TMA, inspired by the previous research presented by EUROCONTROL EEC [1]. On several use-case examples using historical dataset from Stokholm Arlanda airport, we illustrate how to effectively capture different aspects of flight inefficiency, as well as characterize and quantify sequencing effort and aircraft spacing. This is a contribution towards the development of the adaptive multidimensional key performance indicators (KPIs) tailored to the specific aspects of airspace performance, and designed to serve further airspace optimization initiatives.

An implementation of the Remote Tower concept comes with the challenge of optimizing staff resources subject to safety requirements. To distinguish safe from unsafe assignments, the quantification of tower controller workload—which is not a new problem—needs to be reconsidered in the setting of a remote tower environment. We plan to identify the remote operation specific complexity factors, which will be the basis of finding measures that have a high correlation to these factors that together describe the workload. In this paper, we analyze simulation data for these complexity factors. In the simulation different controllers rated the workload while monitoring multiple airports (either with simultaneously visible screens, or switching between the displays). We focus on complexity factors that stem from the interplay of Tower and Ground Control. The resulting list of the most significant complexity factors gives a base for our future quantification of remote tower controller workload. © 2018, SESAR Joint Undertaking. All rights reserved.

Remote tower service is one of the technological and operational solutions delivered for deployment by the Single European Sky Air Traffic Management Research Program. This new concept fundamentally changes how operators provide air traffic services as it becomes possible to control several airports from a single remote center. In such settings, an air traffic controller works at a so-called multiple position in the remote center; that is, he/she handles two or more airports from one remote tower module, that is, the controller working position. In this paper, an optimization framework is presented for traffic management at five Swedish airports that were chosen for remote operation using a remote tower center designed to serve a number of airports. The problems experienced with real airport schedules are highlighted, and optimal assignments of the airports to the remote tower modules are presented. Both scheduled traffic and special (nonscheduled) traffic at these five airports are considered.

We explore use of machine learning in automating the discovery of meaningful time intervals in video data. We combine Convolutional Neural Networks and Principal Component Analysis in order to zoom-in on interesting moments in hours-long videos of air traffic controllers work. Experimental results for air traffic control tower at Stockholm Bromma airport confirm feasibility of our approach. The method may be consequently used to single out workload-influencing factors, incident investigation and other post-operational analysis of controllers performance.

Covid-19 pandemic affected aviation severely, resulting in unprecedented reduction of air traffic. While aviationis slowly re-gaining traffic volumes, we use the opportunity to study the arrival performance in the Terminal Maneuvering Area (TMA) in non-congested scenarios. Applying flight efficiency and environmental performance indicators (PIs) to the historical data of arrivals to Stockholm Arlanda airport, we discover noticeable inefficiencies, despite significant reduction of traffic intensity. We analyse the impact of such factors as weather and traffic intensity on arrival efficiency in isolated scenarios when only one factor dominates. Our analysis uncovers that weather has a stronger influence than congestion on vertical efficiency, while congestion affects both, but mostly lateral efficiency.

An important step towards improving the flight performance within Terminal Maneuvering Area (TMA) is the identification of the factors causing inefficiencies. Without knowing which exact factors have high impact on which performance indicators, it is difficult to identify which areas could be improved. In this work, we quantify the flight efficiency using average additional time in TMA, average time flown level and additional fuel consumption associated with the inefficient flight profiles. We apply statistical learning methods to assess the impact of different weather phenomena on the arrival flight efficiency, taking into account the current traffic situation. We utilize multiple data sources for obtaining both historical flight trajectories and historical weather measurements, which facilitates a comprehensive analysis of the variety of factors influencing TMA performance. We demonstrate our approach by identifying that wind gust and snow had the most significant impact on Stockholm Arlanda airport arrivals in 2018

Bloom filter (BF) based forwarding was proposed recently in several protocol alternatives to IP multicast. Some of these protocols avoid the state in intermediate routers and leave the burden of scalability management to the multicast source and end-hosts. Still, the existing BF-based protocols have scalability limitations and require explicit network management as well as non-trivial functionality from the network components. In this work we address the scalability limitations of the BF-based forwarding protocols by partitioning endhosts into clusters. We propose several algorithms to do the partitioning so as to decrease the overall traffic in the network. We evaluate our algorithms in a real Internet topology, demonstrating the ability of the proposed design to save up to 70% of traffic volume in the large-scale topology for big groups of subscribers, and up to 30% for small groups. (C) 2015 Elsevier B.V. All rights reserved.

This work presents an enhanced optimization framework for fully automated scheduling of energy-efficient continuous-descent arrivals with guaranteed separation in the Terminal Maneuvering Area (TMA). On the example of a real heavy-traffic scenario at Stockholm Arlanda airport, we demonstrate that our approach enables scheduling of all planned arrivals during one hour of operation as continuous descents, by allowing flexible time of arrival to entry points within a range of ± 5 minutes. This provides significant savings in the time aircraft spend inside the TMA and a reduced fuel consumption. In addition, we integrate different aircraft wake turbulence categories that enable category-specific separation criteria. 

Terminal areas often experience significant performance degradation due to limited airspacecapacity and operational resources, which unavoidably contributes to the increased environ-mental footprint of the aviation sector. In this paper, we present our work on the developmentof the new performance evaluation metrics for efficient, fair and comprehensive quantitativeassessment of the arrival operations within the Terminal Maneuvering Areas (TMAs). Weinvestigate existing metrics, develop new ones and study dependencies between them, targetingcreation of the comprehensive performance assessment framework. Using open-source historicalflight data, we test the framework on several airports in Europe which implement differentarrival procedures. The newly introduced performance metrics have a potential to predict theTMA performance based of the characteristics of the arrival flows to TMA.

Point Merge (PM) arrival procedure is implemented at multiple airports around the World. There aredifferent PM design variants: with overlapping, partially overlapping or separated sequencing legs, aposition of the sequencing legs inside or outside the TMA, different geometry of the flows to PM ormerging to a point; each coming with a different impact on the trade-offs associated with the structure.

In this work, we investigate the usage of PM procedures in several airports around the globe using open-access ADS-B-based data provided by the OpenSky Network. We analyse arrival flows at the airports with

PM, and propose a catchment algorithm to see which flights from the blend are actually adherent to theprocedure. Then quantify the PM utilization by applying the performance indicators specifically designedfor this purpose.

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.