Short-term traffic prediction using physics-aware neural networks
Artikel i vetenskaplig tidskrift, 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

Författare

Mike Pereira

Chalmers, Elektroteknik, System- och reglerteknik

Annika Lang

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Balázs Adam Kulcsár

Chalmers, Elektroteknik, System- och reglerteknik

Transportation Research, Part C: Emerging Technologies

0968-090X (ISSN)

Vol. 142 103772

STOchastic Traffic NEtworks (STONE)

Chalmers AI-forskningscentrum (CHAIR), -- .

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Styrkeområden

Transport

Ämneskategorier

Beräkningsmatematik

Transportteknik och logistik

Företagsekonomi

DOI

10.1016/j.trc.2022.103772

Mer information

Senast uppdaterat

2022-09-28