Applied metamodelling for ATM performance simulations
| Title: | Applied metamodelling for ATM performance simulations |
|---|---|
| Authors: | Riis, Christoffer, Antunes, Francisco N., Bolić, Tatjana, Gurtner, Gérald, Cook, Andrew, Azevedo, Carlos Lima, Pereira, Francisco Câmara |
| Publication Year: | 2023 |
| Collection: | Computer Science |
| Subject Terms: | Computer Science - Machine Learning |
| More Details: | The use of Air traffic management (ATM) simulators for planing and operations can be challenging due to their modelling complexity. This paper presents XALM (eXplainable Active Learning Metamodel), a three-step framework integrating active learning and SHAP (SHapley Additive exPlanations) values into simulation metamodels for supporting ATM decision-making. XALM efficiently uncovers hidden relationships among input and output variables in ATM simulators, those usually of interest in policy analysis. Our experiments show XALM's predictive performance comparable to the XGBoost metamodel with fewer simulations. Additionally, XALM exhibits superior explanatory capabilities compared to non-active learning metamodels. Using the `Mercury' (flight and passenger) ATM simulator, XALM is applied to a real-world scenario in Paris Charles de Gaulle airport, extending an arrival manager's range and scope by analysing six variables. This case study illustrates XALM's effectiveness in enhancing simulation interpretability and understanding variable interactions. By addressing computational challenges and improving explainability, XALM complements traditional simulation-based analyses. Lastly, we discuss two practical approaches for reducing the computational burden of the metamodelling further: we introduce a stopping criterion for active learning based on the inherent uncertainty of the metamodel, and we show how the simulations used for the metamodel can be reused across key performance indicators, thus decreasing the overall number of simulations needed. |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2308.03404 |
| Accession Number: | edsarx.2308.03404 |
| Database: | arXiv |
| FullText | Text: Availability: 0 CustomLinks: – Url: http://arxiv.org/abs/2308.03404 Name: EDS - Arxiv Category: fullText Text: View this record from Arxiv MouseOverText: View this record from Arxiv – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsarx&genre=article&issn=&ISBN=&volume=&issue=&date=20230807&spage=&pages=&title=Applied metamodelling for ATM performance simulations&atitle=Applied%20metamodelling%20for%20ATM%20performance%20simulations&aulast=Riis%2C%20Christoffer&id=DOI: Name: Full Text Finder (for New FTF UI) (s8985755) Category: fullText Text: Find It @ SCU Libraries MouseOverText: Find It @ SCU Libraries |
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| Items | – Name: Title Label: Title Group: Ti Data: Applied metamodelling for ATM performance simulations – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Riis%2C+Christoffer%22">Riis, Christoffer</searchLink><br /><searchLink fieldCode="AR" term="%22Antunes%2C+Francisco+N%2E%22">Antunes, Francisco N.</searchLink><br /><searchLink fieldCode="AR" term="%22Bolić%2C+Tatjana%22">Bolić, Tatjana</searchLink><br /><searchLink fieldCode="AR" term="%22Gurtner%2C+Gérald%22">Gurtner, Gérald</searchLink><br /><searchLink fieldCode="AR" term="%22Cook%2C+Andrew%22">Cook, Andrew</searchLink><br /><searchLink fieldCode="AR" term="%22Azevedo%2C+Carlos+Lima%22">Azevedo, Carlos Lima</searchLink><br /><searchLink fieldCode="AR" term="%22Pereira%2C+Francisco+Câmara%22">Pereira, Francisco Câmara</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2023 – Name: Subset Label: Collection Group: HoldingsInfo Data: Computer Science – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+Science+-+Machine+Learning%22">Computer Science - Machine Learning</searchLink> – Name: Abstract Label: Description Group: Ab Data: The use of Air traffic management (ATM) simulators for planing and operations can be challenging due to their modelling complexity. This paper presents XALM (eXplainable Active Learning Metamodel), a three-step framework integrating active learning and SHAP (SHapley Additive exPlanations) values into simulation metamodels for supporting ATM decision-making. XALM efficiently uncovers hidden relationships among input and output variables in ATM simulators, those usually of interest in policy analysis. Our experiments show XALM's predictive performance comparable to the XGBoost metamodel with fewer simulations. Additionally, XALM exhibits superior explanatory capabilities compared to non-active learning metamodels. Using the `Mercury' (flight and passenger) ATM simulator, XALM is applied to a real-world scenario in Paris Charles de Gaulle airport, extending an arrival manager's range and scope by analysing six variables. This case study illustrates XALM's effectiveness in enhancing simulation interpretability and understanding variable interactions. By addressing computational challenges and improving explainability, XALM complements traditional simulation-based analyses. Lastly, we discuss two practical approaches for reducing the computational burden of the metamodelling further: we introduce a stopping criterion for active learning based on the inherent uncertainty of the metamodel, and we show how the simulations used for the metamodel can be reused across key performance indicators, thus decreasing the overall number of simulations needed. – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2308.03404" linkWindow="_blank">http://arxiv.org/abs/2308.03404</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2308.03404 |
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| RecordInfo | BibRecord: BibEntity: Subjects: – SubjectFull: Computer Science - Machine Learning Type: general Titles: – TitleFull: Applied metamodelling for ATM performance simulations Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Riis, Christoffer – PersonEntity: Name: NameFull: Antunes, Francisco N. – PersonEntity: Name: NameFull: Bolić, Tatjana – PersonEntity: Name: NameFull: Gurtner, Gérald – PersonEntity: Name: NameFull: Cook, Andrew – PersonEntity: Name: NameFull: Azevedo, Carlos Lima – PersonEntity: Name: NameFull: Pereira, Francisco Câmara IsPartOfRelationships: – BibEntity: Dates: – D: 07 M: 08 Type: published Y: 2023 |
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