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Authors: Sebastian Bomher 1 and Bogdan Ichim 1 ; 2

Affiliations: 1 Faculty of Mathematics and Computer Science, University of Bucharest, Str. Academiei 14, Bucharest, Romania ; 2 Simion Stoilow Institute of Mathematics of the Romanian Academy, Str. Calea Grivitei 21, Bucharest, Romania

Keyword(s): Traffic Models, Neural Network (NN), Graph Convolutional Network (GCN), Long Short-Term Memory Network (LSTM), Adjacency Matrix.

Abstract: We present in this paper some experiments with the adjacency matrix used as input by three spatio-temporal neural networks architectures when predicting traffic. The architectures were proposed in (Chen et al., 2022), (Li et al., 2018) and (Yu et al., 2018). We find that the predictive power of these neural networks is influenced to a great extent by the inputted adjacency matrix (i.e. the weights associated to the graph of the available traffic infrastructure). The experiments were made using two newly prepared datasets.

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Paper citation in several formats:
Bomher, S. and Ichim, B. (2023). On the Adjacency Matrix of Spatio-Temporal Neural Network Architectures for Predicting Traffic. In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-652-1; ISSN 2184-495X, SciTePress, pages 321-328. DOI: 10.5220/0011971300003479

@conference{vehits23,
author={Sebastian Bomher. and Bogdan Ichim.},
title={On the Adjacency Matrix of Spatio-Temporal Neural Network Architectures for Predicting Traffic},
booktitle={Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2023},
pages={321-328},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011971300003479},
isbn={978-989-758-652-1},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - On the Adjacency Matrix of Spatio-Temporal Neural Network Architectures for Predicting Traffic
SN - 978-989-758-652-1
IS - 2184-495X
AU - Bomher, S.
AU - Ichim, B.
PY - 2023
SP - 321
EP - 328
DO - 10.5220/0011971300003479
PB - SciTePress

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