Short-term traffic prediction using physics-aware neural networks
Preprint, 2021

In this work, we propose an algorithm performing short-term predictions of the flux of vehicles on a stretch of road, using past measurements of the flux. 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 flux predictions based on estimated and predicted space-time dependent traffic parameters. These parameters are themselves obtained using a succession of LSTM ans simple recurrent neural networks. Besides, on top of the predictions, the algorithm yields a smoothing of its inputs which is also physically-constrained by the macroscopic traffic flow model. The algorithm is tested on raw flux measurements obtained from loop detectors.


Mike Pereira

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Annika Lang

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Balázs Adam Kulcsár

Chalmers, Electrical Engineering, Systems and control, Automatic Control

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Computational Mathematics

Probability Theory and Statistics

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