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A Comparative Study of Graph Neural Network Speed Prediction during Periods of Congestion

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Convolutional Neural Networks; Deep Learning; Recurrent Neural Networks

Authors: Marko Oosthuizen 1 ; 2 ; Alwyn J. Hoffman 1 and Marelie Davel 1 ; 2 ; 3

Affiliations: 1 Faculty of Engineering, North-West University, South Africa ; 2 Centre for Artificial Intelligence Research (CAIR), South Africa ; 3 National Institute for Theoretical and Computational Sciences (NITheCS), South Africa

Keyword(s): Traffic Prediction, Congestion, Graph Neural Network.

Abstract: Traffic speed prediction using deep learning has been the topic of many studies. In this paper, we analyse the performance of Graph Neural Network-based techniques during periods of traffic congestion. We first compare a selection of recently proposed techniques that claim to achieve good results using the METR-LA and PeMS-BAY data sets. We then investigate the performance of three of these approaches – Graph WaveNet, Spacetime Neural Network (STNN) and Spatio-Temporal Attention Wavenet (STAWnet) – during congested periods, using recurrent congestion patterns to set a threshold for general congestion through the entire traffic network. Our results show that performance deteriorates significantly during congested time periods, which is concerning, as traffic speed prediction is usually of most value during times of congestion. We also found that, while the above approaches perform almost equally in the absence of congestion, there are much bigger differences in performance during peri ods of congestion. (More)

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Paper citation in several formats:
Oosthuizen, M.; J. Hoffman, A. and Davel, M. (2022). A Comparative Study of Graph Neural Network Speed Prediction during Periods of Congestion. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 331-338. DOI: 10.5220/0011374100003332

@conference{ncta22,
author={Marko Oosthuizen. and Alwyn {J. Hoffman}. and Marelie Davel.},
title={A Comparative Study of Graph Neural Network Speed Prediction during Periods of Congestion},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA},
year={2022},
pages={331-338},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011374100003332},
isbn={978-989-758-611-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA
TI - A Comparative Study of Graph Neural Network Speed Prediction during Periods of Congestion
SN - 978-989-758-611-8
IS - 2184-3236
AU - Oosthuizen, M.
AU - J. Hoffman, A.
AU - Davel, M.
PY - 2022
SP - 331
EP - 338
DO - 10.5220/0011374100003332
PB - SciTePress

- Science and Technology Publications, Lda.
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