Short-term traffic prediction using physics-aware neural networks
Journal article, 2022

In this work, we propose an algorithm performing short-termpredictions 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. Adiscretization of a macroscopic traffic flowmodel (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.

Macroscopic traffic flow model

LSTM

Traffic reaction model

Traffic prediction

Recurrent neural networks

Author

Mike Pereira

Chalmers, Electrical Engineering, Systems and control

Annika Lang

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Balázs Adam Kulcsár

Chalmers, Electrical Engineering, Systems and control

Transportation Research, Part C: Emerging Technologies

0968-090X (ISSN)

Vol. 142 103772

STOchastic Traffic NEtworks (STONE)

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Areas of Advance

Transport

Subject Categories

Computational Mathematics

Transport Systems and Logistics

Business Administration

DOI

10.1016/j.trc.2022.103772

More information

Latest update

9/28/2022