Probing Traffic Trend Forecasting via Spatial-Temporal Aware Learning-Graph Attention

Xinyuan Huang, Qianqian Ren
Proceedings of the 15th Asian Conference on Machine Learning, PMLR 222:486-501, 2024.

Abstract

Traffic forecasting plays an extremely important role in many applications such as intelligent transportation and smart cities. However, due to the hidden and complex dynamic spatio-temporal correlations and heterogeneity, achieving high-precision traffic prediction is a challenging task. This paper proposes a new spatio-temporal aware learning graph neural network (STALGNN) for traffic prediction. First, a temporal-aware graph generation module is designed to exploit the spatial-temporal features that the spatial graph may not be able to present. Then, a spatio-temporal joint module is designed to more effectively capture local spatio-temporal correlations. Next, a multi-scale gated convolutions module is proposed to capture gloable dynamic spatio-temporal correlations. Furthermore, STALGNN further learns explicit spatio-temporal correlations through integrated attention mechanisms and stacked graph convolutional networks to handle long-term prediction. Extensive experiments on several real traffic datasets show that the proposed method can achieve the superior performance compared with other baselines.

Cite this Paper

BibTeX
@InProceedings{pmlr-v222-huang24b, title = {Probing Traffic Trend Forecasting via Spatial-Temporal Aware Learning-Graph Attention}, author = {Huang, Xinyuan and Ren, Qianqian}, booktitle = {Proceedings of the 15th Asian Conference on Machine Learning}, pages = {486--501}, year = {2024}, editor = {Yanıkoğlu, Berrin and Buntine, Wray}, volume = {222}, series = {Proceedings of Machine Learning Research}, month = {11--14 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v222/huang24b/huang24b.pdf}, url = {https://proceedings.mlr.press/v222/huang24b.html}, abstract = {Traffic forecasting plays an extremely important role in many applications such as intelligent transportation and smart cities. However, due to the hidden and complex dynamic spatio-temporal correlations and heterogeneity, achieving high-precision traffic prediction is a challenging task. This paper proposes a new spatio-temporal aware learning graph neural network (STALGNN) for traffic prediction. First, a temporal-aware graph generation module is designed to exploit the spatial-temporal features that the spatial graph may not be able to present. Then, a spatio-temporal joint module is designed to more effectively capture local spatio-temporal correlations. Next, a multi-scale gated convolutions module is proposed to capture gloable dynamic spatio-temporal correlations. Furthermore, STALGNN further learns explicit spatio-temporal correlations through integrated attention mechanisms and stacked graph convolutional networks to handle long-term prediction. Extensive experiments on several real traffic datasets show that the proposed method can achieve the superior performance compared with other baselines.} }
Endnote
%0 Conference Paper %T Probing Traffic Trend Forecasting via Spatial-Temporal Aware Learning-Graph Attention %A Xinyuan Huang %A Qianqian Ren %B Proceedings of the 15th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Berrin Yanıkoğlu %E Wray Buntine %F pmlr-v222-huang24b %I PMLR %P 486--501 %U https://proceedings.mlr.press/v222/huang24b.html %V 222 %X Traffic forecasting plays an extremely important role in many applications such as intelligent transportation and smart cities. However, due to the hidden and complex dynamic spatio-temporal correlations and heterogeneity, achieving high-precision traffic prediction is a challenging task. This paper proposes a new spatio-temporal aware learning graph neural network (STALGNN) for traffic prediction. First, a temporal-aware graph generation module is designed to exploit the spatial-temporal features that the spatial graph may not be able to present. Then, a spatio-temporal joint module is designed to more effectively capture local spatio-temporal correlations. Next, a multi-scale gated convolutions module is proposed to capture gloable dynamic spatio-temporal correlations. Furthermore, STALGNN further learns explicit spatio-temporal correlations through integrated attention mechanisms and stacked graph convolutional networks to handle long-term prediction. Extensive experiments on several real traffic datasets show that the proposed method can achieve the superior performance compared with other baselines.
APA
Huang, X. & Ren, Q.. (2024). Probing Traffic Trend Forecasting via Spatial-Temporal Aware Learning-Graph Attention. Proceedings of the 15th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 222:486-501 Available from https://proceedings.mlr.press/v222/huang24b.html.

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