Inverting the Fundamental Diagram and Forecasting Boundary Conditions: How Machine Learning Can Improve Macroscopic Models for Traffic Flow. | D2R Server publishing the DBLP Bibliography Database, hosted at L3S Research Center

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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Elia_Onofri>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Emiliano_Cristiani>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Maya_Briani>
foaf:homepage	<http://dx.doi.org/doi.org%2F10.48550%2FarXiv.2303.12740>
foaf:homepage	<https://doi.org/10.48550/arXiv.2303.12740>
dc:identifier	DBLP journals/corr/abs-2303-12740 (xsd:string)
dc:identifier	DOI doi.org%2F10.48550%2FarXiv.2303.12740 (xsd:string)
dcterms:issued	2023 (xsd:gYear)
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rdfs:label	Inverting the Fundamental Diagram and Forecasting Boundary Conditions: How Machine Learning Can Improve Macroscopic Models for Traffic Flow. (xsd:string)
foaf:maker	<https://dblp.l3s.de/d2r/resource/authors/Elia_Onofri>
foaf:maker	<https://dblp.l3s.de/d2r/resource/authors/Emiliano_Cristiani>
foaf:maker	<https://dblp.l3s.de/d2r/resource/authors/Maya_Briani>
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rdfs:seeAlso	<https://doi.org/10.48550/arXiv.2303.12740>
dc:title	Inverting the Fundamental Diagram and Forecasting Boundary Conditions: How Machine Learning Can Improve Macroscopic Models for Traffic Flow. (xsd:string)
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