Inverting the fundamental diagram and forecasting boundary conditions: how machine learning can improve macroscopic models for traffic flow

DOI10.1007/s10444-024-10206-8MaRDI QIDQ6660072

Maya Briani, E. Onofri, Emiliano Cristiani

Publication date: 10 January 2025

Published in: Advances in Computational Mathematics (Search for Journal in Brave)

zbMATH Keywords

machine learningLWR modelfundamental diagramvehiclesLSTMhybrid traffic models

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Traffic and pedestrian flow models (76A30)

Cites Work

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• A Heterogeneous Multiclass Traffic Flow Model with Creeping
• Shock Waves on the Highway
• On kinematic waves II. A theory of traffic flow on long crowded roads
• Data-driven models for traffic flow at junctions

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