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 103772STOchastic Traffic NEtworks (STONE)
Chalmers, 2020-02-01 -- 2022-01-31.
Chalmers AI-forskningscentrum (CHAIR), -- .
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Real-Time Robust and AdaptIve Learning in ElecTric VEhicles (RITE)
Chalmers, 2020-01-01 -- 2021-12-31.
Chalmers AI-forskningscentrum (CHAIR), 2020-01-01 -- 2021-12-31.
Styrkeområden
Transport
Ämneskategorier
Beräkningsmatematik
Transportteknik och logistik
Företagsekonomi
DOI
10.1016/j.trc.2022.103772