Interactive Toolset for Understanding Trade-offs in ATM Performance

Informazioni relative al progetto

INTUIT

ID dell’accordo di sovvenzione: 699303

DOI

10.3030/699303

Progetto chiuso

Data della firma CE 17 Febbraio 2016

Data di avvio 1 Marzo 2016

Data di completamento 30 Aprile 2018

Finanziato da

• SOCIETAL CHALLENGES - Smart, Green And Integrated Transport

Costo totale

€ 998 125,00

Contributo UE

€ 998 125,00

Coordinato da

NOMMON SOLUTIONS AND TECHNOLOGIES SL
Spain

Periodic Reporting for period 4 - INTUIT (Interactive Toolset for Understanding Trade-offs in ATM Performance)

Periodo di rendicontazione: 2017-09-01 al 2018-04-30

Sintesi del contesto e degli obiettivi generali del progetto

The ongoing ATM modernisation programmes, including SESAR, build on ICAO Global ATM Operational Concept, one of whose cornerstones is performance orientation. A performance-based approach is defined by ICAO as one based on: (i) strong focus on desired/required results; (ii) informed decision making, driven by the desired/required results; and (iii) reliance on facts and data for decision making. While a lot of effort has traditionally been devoted to the development of microscopic performance models, there is a lack of useful macro approaches able to translate local improvements or specific regulations into their impact on high-level, system-wide KPIs. The goal of INTUIT was to explore the potential of data science to improve our understanding of the trade-offs between ATM KPAs, identify cause-effect relationships between indicators at different scales, and develop new decision support tools for ATM performance monitoring and management. The specific outputs of the project include:
1. a detailed review of available databases relevant for ATM performance research;
2. a list of research questions at the intersection of ATM performance and data science;
3. a set of new modelling approaches and interactive visualisation tools for ATM performance analysis, focused on three specific applications: (i) modelling of airline route choices and their influence on ATM performance; (ii) identification of sources of en-route flight inefficiency; and (iii) multi-scale representation of performance data.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

The project started by identifying the main data sources on ATM performance and assessing the validity, quality, and geographical and temporal resolution of each dataset. This work produced three main outcomes: a set of Performance Data Factsheets characterising each data source, a Performance Data Guide which links ATM performance data with the sources where such data can be found, and the INTUIT Data Repository, which allowed the project partners to share the datasets used for the INTUIT research work. These outcomes are documented in deliverable D2.1.

Taking this work as a starting point, a combination of literature review and stakeholder consultation allowed the identification of a list of relevant research questions at the intersection of ATM performance modelling and data science, documented in D2.2. Based on a combination of factors, including the relevance of the research question, the expected impact of the results, the availability of sufficient data and the potential of data science to advance the state-of-the-art in that particular field, a subset of these research questions was selected to be investigated in the form of three Case Studies (CS):
- CS-1: Effect of unit rates on airline route choices and impact on ATM performance. The goal was to develop new models able to predict airline route choices between different ODs in order to evaluate the performance trade-offs arising from these decisions (e.g. cost efficiency vs environment). The proposed approach has shown significant potential to improve the understanding of route choices, and it can be applied to pre-tactical traffic forecast.
- CS-2: Sources of en-route flight inefficiency. This case study, conducted in collaboration with the SESAR ER projects AURORA and APACHE, investigated the causes of inefficient routes in the European Network and their effects on performance by means of a machine learning algorithm that predicts en-route flight efficiency, in order to isolate the contribution of different factors and stakeholders.
- CS-3: Multi-scale representation of ATM performance indicators. This case study aimed to disaggregate traffic data and performance indicators at ACC and sector level and to model the relationship between these variables at different scales (e.g. what is the influence of sector configuration on the aggregated performance of an ANSP?).

In order to explore the datasets used for the case studies, different visualisation and visual analytics tools were developed. This work is documented in D3.1 Visual Analytics Exploration of Performance Data. The performance modelling work is documented in D4.1 Performance Metrics and Predictive Models. Finally, the new visualisations and modelling techniques were integrated into an interactive performance monitoring and management dashboard, including: (i) a multi-objective optimisation engine to find the Pareto optimal solution for a set of KPIs. The tool allows the estimation of the effects of a particular setting of unit rates and helps in the selection of the best setting of unit rates to optimise the trade-off between flight efficiency, cost efficiency and capacity, (ii) a flight efficiency monitoring dashboard to identify and evaluate the causes of flight inefficiency in a particular ACC. The results of this work are documented in D5.1 Performance Monitoring and Management Toolset and D5.2 Performance Monitoring and Management Toolset Evaluation Report.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

INTUIT has analysed the main databases regarding performance data in the ECAC area. This work, documented in D2.1 and D2.2 will be helpful for other research projects to select the data necessary for their research. The project has also performed a thorough literature review and an extensive stakeholder consultation to select the most relevant research threads in the field of ATM performance modelling, which has led to the work documented in deliverables D3.1 and D4.1. The development of a route choice predictor has provided an enhanced understanding of Airspace Users behaviour, which can in turn contribute to improving performance consolidation methodologies and Cost Benefit Analysis (CBA). Additionally, the models have a potential for pre-tactical traffic forecast and could thus contribute to enhancing pre-tactical ATFCM. The assessment of the causes and effects of en-route flight inefficiency will enhance ATM performance management by allowing the quantification of the impact of the different influence factors. As an example application, the proposed approach can be used to investigate how much of the flight inefficiency observed in a certain ACC is due to suboptimal airspace design and to propose mitigation measures (e.g. improved design of the interfaces between adjacent airspace areas). In summary, INTUIT has developed a structured approach and a set of prototype performance analysis tools that have demonstrated the potential of visual analytics and machine learning to improve the state-of-the-art in ATM performance analysis. The contributions to the improvement of the cost-efficiency and the quality of service of the ATM system will ultimate benefit all the stakeholders of the aviation sector. Additionally, the INTUIT partners have taken advantage of the project to take a leading position in the application of data analytics to ATM performance analysis. The INTUIT results are expected to feed into the subsequent stages of the R&I lifecycle and be the basis for the future development of new products and services for ATM performance monitoring and management.

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