@article{ref1,
title="Short-term traffic prediction using physics-aware neural networks",
journal="Transportation research part C: emerging technologies",
year="2022",
author="Pereira, Mike and Lang, Annika and Kulcsár, Balázs",
volume="142",
number="",
pages="e103772-e103772",
abstract="In this work, we propose an algorithm performing short-term predictions 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. 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 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.<p /> <p>Language: en</p>",
language="en",
issn="0968-090X",
doi="10.1016/j.trc.2022.103772",
url="http://dx.doi.org/10.1016/j.trc.2022.103772"
}