ONBOARD

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ONBOARD Probabilistic Network Based Operations ATM R&D

Contents

About

This project aims to improve the performance of the ATM system in the short term planning phase by developing new models and algorithms to enable the Network Manager to better manage the two factors that account for two thirds of the ATFM delay in Europe (weather and knock-on effects), in particular by addressing the key sources of uncertainty (weather forecast, unscheduled demand, air users response to disruptions). A Network Manager and AOC (Airline Operations Centre) simulation prototypes (together with their underlying models and algorithms) was developed and integrated into an Evaluation Platform to carry out a set of evaluation exercises to assess the achievable ATM performances benefits. Robust techniques unused before in the ATM domain have been used to solve the demand and capacity problem under uncertainty in operationally representative (e.g. problem size, computation time) conditions.

Objectives

One of the difficulties in improving the performance of ATFM through optimization is the presence of uncertainty. Future capacity state predictions are inherently uncertain due to factors such as weather effects and unscheduled demand but neither in current practice nor the SESAR concept of operations information that could be available on the uncertainty associated with the system is used. The goal of the ONBOARD project has been to see if improvements can be made in ATFM performance by explicitly incorporating information about uncertainty within the optimization in the Network Management planning and execution phases. Furthermore, the ONBOARD project has also focused on addressing the two factors that jointly account nowadays for two thirds of the total ATFM delay in Europe: weather and knock-on effects.

Methodology

The approach followed in the project to research on these key issues (uncertainty due to weather and unscheduled demand, on the one hand, and knock-off effects, on the other) has consisted of developing two interacting algorithms, one acting as the Airline Operations Centre (AOC) and the other as the Network Manager (NM), being in charge of managing the knock-on effects and the weather and unscheduled demand uncertainty, respectively. The concept of disturbance feedback from control research (MPC) is applied within the NM to handle uncertainty information on unscheduled demand and weather forecast. The formulation developed introduce, in the control optimization, the notion that one can have a fundamental plan based on the idea that feedback is present in the control implementation, meaning that the resulting action will take into account the effects of possible future disturbances. The solutions produced via this methodology within the NM are then iterated with the AOC providing alternative recovery plans for further iterations. To that end the AOC models in a realistic way the knock-on effects caused by the rotational reactionary delays introduced in the system by late aircraft arrivals: in particular, it solves in the event of disruptions (e.g. due to the time constraints imposed by the Network Manager) the integrated problem of airline operations and control, i.e. to determine simultaneously the optimum aircraft rotation plan and the optimum set of flight plans (i.e. trip fuel and 4D trajectory) for each flight leg of the airline schedule, that minimizes the airline operations costs, including the costs of disruption recovery (e.g. passengers compensation due to departure delay).

Simulation Platform

To be able to obtain realistic results out of the project, those two brand new algorithms have been integrated into a simulation platform developed ad hoc, consisting of a set of databases to exchange data, several HMIs to control the different processes and a Radar HMI that allows the researcher to follow the simulation from the perspective of an eventual sub-regional Network Manager.

The platform consists of two main components, the Network Manager (NM) and the Airline Operations Centre (AOC). The Network Management algorithm is the core research goal of the project as this is where the uncertainty will be incorporated. The Airline Operation Centre algorithm is necessary in the project to interact with the Network Management algorithm and pursuits its own research challenges.

Airline Operations Centre

The main role of the Airline Operations Centre (AOC) algorithm is to calculate the necessary airspace user recovery plans to cope with adverse scenarios (e.g. significant traffic congestion at an airport or at an airspace volume), by updating the aircraft rotation plan (e.g. delaying, re-routing or cancelling flights; swapping slots) and retiming part of the flights schedule until the original flight schedule can be resumed.

Network Manager

The focus of the Network Manager (NM) is on the subset of ATM which deals with allocating airspace resources such that the balance between capacity and demand is maintained in the presence of both enroute and airport capacity constraints. This is known as Air Traffic Flow Management (ATFM) and many studies have applied optimization to the problem to find the best solution (subject to some objective).

The scope of the system considered covers airport departure and arrival capacity limits at airports as well as enroute sector capacity limits. Control actions available are delays to the arrival, departure, and sector crossing times. Modelling of ATFM problems can broadly be divided into three categories: discrete decision models (sometimes referred to as Lagrangian models) which consider the individual plan of each aircraft in the problem (Flight-by-flight); aggregate flow models (sometimes referred to as Eulerian models) which consider the flow rates and densities in control volumes but do not track individual aircraft plans; and hybrids of the two (Eulerian-Lagrangian), which augment aggregate models to include some knowledge of individual flights. The NM adopts an Eulerian-Lagrangian or primarily flowbased ATFM viewpoint, meaning that a separate optimization stage is required to disaggregate the solution.

The baseline flow based optimization model implemented is a slight reformulation of the model presented in Sun and Bayen which was inspired by the Lighthill-Whitham-Richards theory and by the Daganzo cell transmission model commonly used in highway traffic.

Validation

An extensive validation has been carried out in the project, in particular to characterize the maximum size of the traffic problem that can be addressed with the algorithmic framework developed (e.g. 20 en-route sectors, 350 flights, 15 uncertainty scenarios) applying a rolling planning window approach to address a full day of operations (e.g. with a 30 minutes planning step)

Future Research Lines and Conclusions

The structure of a combined AOC/NM ATFM system has been developed and tested in this project. The integration of the two parts of the systems has produced coherent results. The AOC prototype shows clear benefits in terms of operation cost optimization and demonstrates that recovery costs are reduced significantly if airborne alternatives are considered. The disturbance feedback approach implemented in the NM presents important benefits compared to the alternatives studied in terms of less delay without capacity breaks.

Further development is suggested in order to implement the platform under a rolling window operation mode, industrialize the NM and/or operate it in a more powerful system to be able to cover larger problems and scenarios.

Publications

  • L.J. Álvarez, J. Cegarra, and A.G. Richards, “Network Management under uncertainty, The ONBOARD project: research objectives and current status”; in First SESAR Innovation Day, no. December 2011, pp.1-7.
  • Gilian Clare, A. Richards, “Air Traffic Flow Management Under Uncertainty: Application of Chance Constraints” in Second International Conference ATACCS, no. May, 2012, pp. 20-26.
  • Presentation on “ONBOARD”. GMV. SESAR Joint Undertaking WP-E Information Day. 12/06/2012.
  • G. Clare, A.G. Richards, J. Escartín, D. Martínez, J. Cegarra, L. J. Álvarez, “Air Traffic Flow Management under Uncertainty: Interactions Between Network Manager and Airline Operations Centre” in Second SESAR Innovation Day, no. November, 2012, pp. 1-8
  • G. Clare, A.G. Richards, “Disturbance Feedback for Handling Uncertainty in Air Traffic Flow Management”, European Control Conference, 2013.

Research Team

Coordinator: GMV

Video

YouTube video

Partners: University of Bristol, Skysoft

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