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• W3200553661 abstract "In this work, we propose an algorithm performing short-term predictions of the flow and speed of vehicles on a stretch of road, using past measurements of these quantities. This algorithm is based on a physics-aware recurrent neural network. A discretization of a macroscopic traffic flow model (using the so-called Traffic Reaction Model) is embedded in the architecture of the network and yields traffic state estimations and predictions for the flow and speed of vehicles, which are physically-constrained by the macroscopic traffic flow model and based on estimated and predicted space–time dependent traffic parameters. These parameters are themselves obtained using a succession of LSTM recurrent neural networks. The algorithm is tested on raw flow measurements obtained from loop detectors." @default.
• W3200553661 created "2021-09-27" @default.
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• W3200553661 date "2022-09-01" @default.
• W3200553661 modified "2023-10-16" @default.
• W3200553661 title "Short-term traffic prediction using physics-aware neural networks" @default.
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• W3200553661 doi "https://doi.org/10.1016/j.trc.2022.103772" @default.
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