Quantum neural networks with data re-uploading for urban traffic time series forecasting

Abstract Accurate traffic forecasting plays a crucial role in modern Intelligent Transportation Systems (ITS), as it enables real-time traffic flow management, reduces congestion, and improves the overall efficiency of urban transportation networks. With the rise of Quantum Machine Learning (QML), i...

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Hlavní autoři:	Nikolaos Schetakis, Paolo Bonfini, Negin Alisoltani, Konstantinos Blazakis, Symeon I. Tsintzos, Alexis Askitopoulos, Davit Aghamalyan, Panagiotis Fafoutellis, Eleni I. Vlahogianni
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Nature Portfolio 2025-06-01
Edice:	Scientific Reports
On-line přístup:	https://doi.org/10.1038/s41598-025-04546-8
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