Dynamic Demand-Capacity Balancing for Air Traffic Management Using Constraint-Based Local Search: First Results

Using constraint-based local search, we effectively model and efficiently solve the problem of balancing the traffic demands on portions of the European airspace while ensuring that their capacity constraints are satisfied. The traffic demand of a portion of airspace is the hourly number of flights planned to enter it, and its capacity is the upper bound on this number under which air-traffic controllers can work. Currently, the only form of demand-capacity balancing we allow is ground holding, that is the changing of the take-off times of not yet airborne flights. Experiments with projected European flight plans of the year 2030 show that already this first form of demand-capacity balancing is feasible without incurring too much total delay and that it can lead to a significantly better demand-capacity balance

Optimizing Flight Departure Delay and Route Selection Under En Route Convective Weather

This paper presents a linear Integer Programming model for managing air traffic flow in the United States. The decision variables in the model are departure delays and predeparture reroutes of aircraft whose trajectories are predicted to cross weather-impacted regions of the National Airspace System. The model assigns delays to a set of flights while ensuring their trajectories are free of any conflicts with weather. In a deterministic setting, there is no airborne holding due to unexpected weather incursion in a flight s path. The model is applied to solve a large-scale traffic flow management problem with realistic weather data and flight schedules. Experimental results indicate that allowing rerouting can reduce departure delays by nearly 57%, but it is associated with an increase in total airborne time due to longer routes flown by aircraft. The computation times to solve this problem were significantly lower than those reported in the earlier studies

Complexity challenges in ATM

After more than 4 years of activity, the ComplexWorld Network, together with the projects and PhDs covered under the SESAR long-term research umbrella, have developed sound research material contributing to progress beyond the state of the art in fields such as resilience, uncertainty, multi-agent systems, metrics and data science. The achievements made by the ComplexWorld stakeholders have also led to the identification of new challenges that need to be addressed in the future. In order to pave the way for complexity science research in Air Traffic Management (ATM) in the coming years, ComplexWorld requested external assessments on how the challenges have been covered and where there are existing gaps. For that purpose, ComplexWorld, with the support of EUROCONTROL, established an expert panel to review selected documentation developed by the network and provide their assessment on their topic of expertise

DEMAND-RESPONSIVE AIRSPACE SECTORIZATION AND AIR TRAFFIC CONTROLLER STAFFING

This dissertation optimizes the problem of designing sector boundaries and assigning air traffic controllers to sectors while considering demand variation over time. For long-term planning purposes, an optimization problem of clean-sheet sectorization is defined to generate a set of sector boundaries that accommodates traffic variation across the planning horizon while minimizing staffing. The resulting boundaries should best accommodate traffic over space and time and be the most efficient in terms of controller shifts. Two integer program formulations are proposed to address the defined problem, and their equivalency is proven. The performance of both formulations is examined with randomly generated numerical examples. Then, a real-world application confirms that the proposed model can save 10%-16% controller-hours, depending on the degree of demand variation over time, in comparison with the sectorization model with a strategy that does not take demand variation into account. Due to the size of realistic sectorization problems, a heuristic based on mathematical programming is developed for a large-scale neighborhood search and implemented in a parallel computing framework in order to obtain quality solutions within time limits. The impact of neighborhood definition and initial solution on heuristic performance has been examined. Numerical results show that the heuristic and the proposed neighborhood selection schemes can find significant improvements beyond the best solutions that are found exclusively from the Mixed Integer Program solver's global search. For operational purposes, under given sector boundaries, an optimization model is proposed to create an operational plan for dynamically combining or splitting sectors and determining controller staffing. In particular, the relation between traffic condition and the staffing decisions is no longer treated as a deterministic, step-wise function but a probabilistic, nonlinear one. Ordinal regression analysis is applied to estimate a set of sector-specific models for predicting sector staffing decisions. The statistical results are then incorporated into the proposed sector combination model. With realistic traffic and staffing data, the proposed model demonstrates the potential saving in controller staffing achievable by optimizing the combination schemes, depending on how freely sectors can combine and split. To address concerns about workload increases resulting from frequent changes of sector combinations, the proposed model is then expanded to a time-dependent one by including a minimum duration of a sector combination scheme. Numerical examples suggest there is a strong tradeoff between combination stability and controller staffing

A Distributed Trajectory-Oriented Approach to Managing Traffic Complexity

In order to handle the expected increase in air traffic volume, the next generation air transportation system is moving towards a distributed control architecture, in which ground-based service providers such as controllers and traffic managers and air-based users such as pilots share responsibility for aircraft trajectory generation and management. While its architecture becomes more distributed, the goal of the Air Traffic Management (ATM) system remains to achieve objectives such as maintaining safety and efficiency. It is, therefore, critical to design appropriate control elements to ensure that aircraft and groundbased actions result in achieving these objectives without unduly restricting user-preferred trajectories. This paper presents a trajectory-oriented approach containing two such elements. One is a trajectory flexibility preservation function, by which aircraft plan their trajectories to preserve flexibility to accommodate unforeseen events. And the other is a trajectory constraint minimization function by which ground-based agents, in collaboration with air-based agents, impose just-enough restrictions on trajectories to achieve ATM objectives, such as separation assurance and flow management. The underlying hypothesis is that preserving trajectory flexibility of each individual aircraft naturally achieves the aggregate objective of avoiding excessive traffic complexity, and that trajectory flexibility is increased by minimizing constraints without jeopardizing the intended ATM objectives. The paper presents conceptually how the two functions operate in a distributed control architecture that includes self separation. The paper illustrates the concept through hypothetical scenarios involving conflict resolution and flow management. It presents a functional analysis of the interaction and information flow between the functions. It also presents an analytical framework for defining metrics and developing methods to preserve trajectory flexibility and minimize its constraints. In this framework flexibility is defined in terms of robustness and adaptability to disturbances and the impact of constraints is illustrated through analysis of a trajectory solution space with limited degrees of freedom and in simple constraint situations involving meeting multiple times of arrival and resolving a conflict

Coordinated and robust aviation network resource allocation

In the United States, flight operators may schedule flights to most airports at whatever time best achieves their objectives. However, during some time periods, both at airports and in the airspace, these freely-developed schedules may become infeasible because weather or other factors reduce capacity. A plan must then be implemented to mitigate this congestion safely, efficiently, and equitably. Current planning processes treat each congested resource independently, applying various rules to increase interoperation times sufficiently to match the reduced capacity. However, several resources are occasionally congested simultaneously, and ignoring possible dependencies may yield infeasible allocations for flights using multiple resources. In this dissertation, this problem of developing coordinated flight-slot allocations for multiple congested resources is considered from several perspectives. First, a linear optimization model is developed. It is demonstrated that optimally minimizing flight arrival delays induces an increasing bias against flights using multiple resources. However, the resulting allocations reduce overall arrival delay, as compared to the infeasible independent allocations, and to current operational practice. The analytic properties of the model are used to develop a rule-based heuristic for allocating capacity that achieves comparable aggregate results. Alternatively, minimizing delay assigned at all resources is considered, and this objective is shown to mimic the flights' original schedule order. Recognizing that minimizing arrival delays is attractive because of its tangible impact on system performance, variations to the original optimization model are proposed that constrain the worst-case performance of any individual user. Several different constraints and cost-based approaches are considered, all of which are successful to varying degrees in limiting inequities. Finally, the model is reformulated to consider uncertainty in capacity. This adds considerable complexity to the formulation, and introduces practical difficulties in identifying joint probability distributions for the capacity outcomes at each resource. However, this new model is successful in developing more robust flight-slot allocations that enable quick responses to capacity variations. Each of the optimization models and heuristics presented here are tested on a realistic case study. The problem studied and the approaches employed represent an important middle ground in air traffic flow management research between single resource models and comprehensive ones

Engage D1.2 Final Project Results Report

This deliverable summarises the activities and results of Engage, the SESAR 2020 Knowledge Transfer Network (KTN). The KTN initiated and supported multiple activities for SESAR and the European air traffic management (ATM) community, including PhDs, focused catalyst fund projects, thematic workshops, summer schools and the launch of a wiki as the one-stop, go-to source for ATM research and knowledge in Europe. Key throughout was the integration of exploratory and industrial research, thus expediting the innovation pipeline and bringing researchers together. These activities laid valuable foundations for the SESAR Digital Academy

Trajectory-Oriented Approach to Managing Traffic Complexity: Operational Concept and Preliminary Metrics Definition

This document describes preliminary research on a distributed, trajectory-oriented approach for traffic complexity management. The approach is to manage traffic complexity in a distributed control environment, based on preserving trajectory flexibility and minimizing constraints. In particular, the document presents an analytical framework to study trajectory flexibility and the impact of trajectory constraints on it. The document proposes preliminary flexibility metrics that can be interpreted and measured within the framework

Autonomous Flight Rules - A Concept for Self-Separation in U.S. Domestic Airspace

Autonomous Flight Rules (AFR) are proposed as a new set of operating regulations in which aircraft navigate on tracks of their choice while self-separating from traffic and weather. AFR would exist alongside Instrument and Visual Flight Rules (IFR and VFR) as one of three available flight options for any appropriately trained and qualified operator with the necessary certified equipment. Historically, ground-based separation services evolved by necessity as aircraft began operating in the clouds and were unable to see each other. Today, technologies for global navigation, airborne surveillance, and onboard computing enable the functions of traffic conflict management to be fully integrated with navigation procedures onboard the aircraft. By self-separating, aircraft can operate with more flexibility and fewer restrictions than are required when using ground-based separation. The AFR concept is described in detail and provides practical means by which self-separating aircraft could share the same airspace as IFR and VFR aircraft without disrupting the ongoing processes of Air Traffic Control