Short-term traffic prediction using physics-aware neural networks

Annika Lang (CUT, Gothenburg)

Jun 02. 2022, 09:30 — 10:00

An algorithm performing short-term predictions of the flux of vehicles on a stretch of road is presented, using past measurements of the flux. This algorithm is based on a physics-aware recurrent neural network which combines the discretization of a macroscopic traffic flow model by the so-called Traffic Reaction Model with the prediction of the space-time dependent traffic parameters by a succession of LSTM and simple RNNs. Besides, the algorithm yields smoothing of its inputs which allows it to handle noisy and faulty raw data without preprocessing. The algorithm is tested on raw flux measurements obtained from loop detectors.

The talk is based on joint work with Mike Pereira and  Balázs Kulcsár presented in arXiv:2109.10253.

Further Information
Venue:
ESI Boltzmann Lecture Hall
Associated Event:
Computational Uncertainty Quantification: Mathematical Foundations, Methodology & Data (Thematic Programme)
Organizer(s):
Clemens Heitzinger (TU Vienna)
Fabio Nobile (EPFL Lausanne)
Robert Scheichl (U Heidelberg)
Christoph Schwab (ETH Zürich)
Sara van de Geer (ETH Zürich)
Karen Willcox (U of Texas, Austin)