NEWO

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Abstract

The SESAR WP-E project NEWO (emerging NEtwork-Wide Effects of inventive Operational approaches in ATM) had explored network-wide performance and delay propagation phenomena in the Air Transport Network linked to specific flight prioritisation strategies. To this end, NEWO had used innovative modelling and simulation techniques.

Introduction

NEWO had set the attention on complexity science and on complex networks different than the air transport one to explore some of the still blurry aspects of the Air Transport Network behaviour. The project performed an outlook to gather criteria to prioritise the distribution of elements in a complex network in case of severe capacity shortfall of the network nodes/edges. Rules commonly used in other domains to manage the network to best match the available capacity were innovative rules applicable to the air transport case. Moreover, the project sought to stimulate the production of innovative operational approaches for prioritisation of departure flights and airline operational strategies.

In this context, innovation finally come from the generation of deeper knowledge of the impact of certain operational rules or from new applications of existing management approaches.

The way NEWO had explored the definition of common and transparent rules for the prioritisation of flights and their translation into new operational approaches had been by means of literature reviews, questionnaires and direct interviews with experts from diverse domains. As for the study of the complex behaviour of the Air Transport Network, NEWO had explored the potential of innovative modelling and simulation techniques through an innovative computational model. The Performance Framework addressed had been oriented to the assessment of network-wide delay performance linked to innovative flight prioritisation criteria.

Capturing prioritisation strategies

In the context of ATM, when an imbalance between forecasted traffic and available capacity is detected, it is usually the ATM authority that imposes a regulation, which aims at protecting the potentially overloaded node by imposing delay on some flights. The flights are usually prioritised on a First Come First Served (FCFS) basis, meaning that the flight which planned to use the resource earlier receives priority on another flight which planned to use it later. In this way delay is imposed without regarding users-preferences, but just on the base of a generally accepted concept of equity among users.

In the first project Workshop, experts from different fields of knowledge such as logistics, complexity science and ATM discussed about how the air transport system could better deal with the complexity of interrelations of its elements with the aim of efficiently performing distribution of passengers and cargo. The discussions led to the identification of a set of promising flight prioritisation strategies (criteria) and different reference scenarios to be tested in.

Modelling approach

The simulations were carried out by means of ATM NEMMO, a simulation tool based on techniques from the complex system field. Regarding uncertainty modelling, the approach adopted by ATM NEMMO was to combine both simulations and statistical parameter evaluation methods. A statistical evaluation at the micro level is used for the modelling of different elements and the randomisation of the simulation model, and then Monte Carlo simulations are used to estimate the probability distribution of a number of local and global indicators.

The tool reproduced a reduced set of nodes (airports and airspace high density areas) of the European Air Transport Network. The selection of the nodes comprises the main 133 European airports and the rest of nodes were represented in an aggregated manner.

For each node, nominal capacity reflected the actual capacity of the airport at each Time Step (TStep), whereas predicted capacity was used to simulate inaccuracies on the information available in the network about the actual capacity of the nodes. Finally, the model created a Network Operations Plan (NOP), derived from the input traffic sample which defines the network structure.

Internal disturbances account for all the potential sources of uncertainty internal to the air transport system. They were modelled as aggregated parameters whose values were obtained from a statistical analysis of delay data.

The model process named ‘UNBALANCE TRAFFIC’ introduced the uncertainty of demand. The reason to include this process was to reflect changes in schedule that occurs in the medium/short-term planning phases. These changes were motivated by increased availability of accurate weather predictions, traffic demand, Air Navigation Service Providers (ANSPs) and airport capacities, etc. The consequence was an unbalanced traffic demand with regards to capacity as input for the execution phase, during which tactical Demand and Capacity Balance (DCB) measures were applied to adapt demand to the available capacity.

The defined flight prioritisation strategies were modelled by plugging-in specific algorithms in the flight execution flow of the model. The model computed the characteristics of flights and/or destination airports that determine the level of priority of each flight, so that they can be sorted out according to the defined criteria. Examples were based on giving priority to flights to more/less congested airports, to airlines with hub &spoke route structure over point to point, or to flights with the higher number of subsequent flight legs.

Conclusions

The expected rapid increase of traffic in European and worldwide airspace will challenge the current structure of the ATM system. The most critical aspect of this challenge is how to face the problem of airspace capacity limitation by ensuring high levels of efficiency together with the highest standards of safety.

Project simulation results had provided a better perception of the way forward for studying the impact of foreseen operational changes in the future Air Transport Network in terms of deepening in the analysis of the network response to specific local stimuli.

Simulating longer periods of time (2 or 3 days operation) will permit to observe if network effects are softened or propagated delays absorbed, when sufficient time has elapsed since the occurrence of an external disturbance.

The set of promising prioritisation strategies gathered during the identification phase and the whole set of simulation results remain as a useful repository for future projects picking up the baton of the flight prioritisation challenge.

In fact, from SESAR and NextGen they are spurring fresh interest from business aircraft operators to upgrade their systems since this improvement on their equipage level will address operational benefits for the aircraft/airlines; beyond the mandates, operators are coming to see the operational benefits these advanced avionics systems can provide.

The prioritisation challenge that better represents the intent of optimizing the efficiency of airspace operations is the Most Capable Best Served (MCBS) criterion; although project preliminary results on this matter are not conclusive enough to arrive at any firm conclusion, it is quite clear that the research work on prioritisation should be focused on this way.

A better understanding of the plan for progressively improving both airlines’ aircraft equipage levels and ANSPs systems will clearly have a significant impact on the planning of future MCBS scenario for network wide impact assessment.

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