SDSTGCN: sparse directed spatio-temporal graph neural network for traffic flow prediction

Qiang Xing; Xinghao Wang; Huimin Xiao

doi:10.1117/12.3012475

21 December 2023 SDSTGCN: sparse directed spatio-temporal graph neural network for traffic flow prediction

Qiang Xing, Xinghao Wang, Huimin Xiao

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297045 (2023) https://doi.org/10.1117/12.3012475
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Traffic flow prediction is a challenging task due to the intricate spatiotemporal dependencies among different traffic patterns. Previous approaches based on graph neural networks often modeled these dependencies as undirected graphs, which is not in line with reality. In fact, the dependencies between traffic flow sequences are inherently directional and dynamic over time. To address these issues, we propose a novel sparse directed graph convolution model, referred to as SDSTGCN. By incorporating self-attention mechanisms and asymmetric spatiotemporal convolutions, we accurately capture the directional dependencies between sensor nodes and effectively model the hidden spatiotemporal relationships. Furthermore, we apply a sparsification technique to eliminate the redundant noise introduced by the self-attention mechanisms. Extensive numerical evaluations on three real-world datasets demonstrate that our proposed method achieves state-of-the-art performance, significantly outperforming the baseline methods.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiang Xing, Xinghao Wang, and Huimin Xiao "SDSTGCN: sparse directed spatio-temporal graph neural network for traffic flow prediction", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297045 (21 December 2023); https://doi.org/10.1117/12.3012475

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available