Short-term traffic prediction using physics-aware neural networks

• URL: http://arxiv.org/abs/2109.10253v1
• Date: Tue, 21 Sep 2021 15:31:33 GMT
• Title: Short-term traffic prediction using physics-aware neural networks
• Authors: Mike Pereira, Annika Lang, and Bal\'azs Kulcs\'ar
• Abstract summary: We propose an algorithm performing short-term predictions of the flux of vehicles on a stretch of road. The algorithm is based on a physics-aware recurrent neural network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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