Spatial-temporal bottleneck attention transformer networks for traffic  flow forecasting

Yun Peng; Wei Sun

doi:10.1117/12.3040366

28 August 2024 Spatial-temporal bottleneck attention transformer networks for traffic flow forecasting

Yun Peng, Wei Sun

Proceedings Volume 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024); 132515K (2024) https://doi.org/10.1117/12.3040366
Event: 9th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 2024, Guilin, China

Abstract

Accurate traffic flow prediction helps managers in travel and decision making. Most of the existing models are based on graph neural networks (GNN), which solve the problem by capturing the spatial dependencies of fixed graph structures. However, this approach is limited due to the incompleteness of data with dynamic spatio-temporal dependencies. More often than not, with more prediction nodes with increasing time span, these types of models are not ideal in terms of efficiency in prediction. To overcome these limitations, this paper proposes a new model: spatio-temporal bottleneck attention Transformer network (STBAN Transformer) for spatio-temporal relationship modeling and long term traffic prediction. Transformer models sequences through the mechanism of self-attention, and by applying it to traffic flow prediction, it can capture the nodes and other nodes well The spatial correlation between nodes and other nodes can be well captured, which is very suitable for the extraction of spatial features of traffic network. In addition, in temporal correlation modeling, we also design an efficient temporal bottleneck attention module to obtain temporal attention in the global spatio-temporal state with low complexity. Experimental results on two public transportation datasets show that our method achieves state-of-the-art performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yun Peng and Wei Sun "Spatial-temporal bottleneck attention transformer networks for traffic flow forecasting", Proc. SPIE 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 132515K (28 August 2024); https://doi.org/10.1117/12.3040366

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KEYWORDS

Transformers

Data modeling

Matrices

Feature extraction

Machine learning

Feature fusion

Modeling

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