Research on traffic flow prediction based on spatio-temporal convolutional networks with attention mechanism

Hong Zhang; Tian-xin Zhao; Jie Cao; Su-nan Kan

doi:10.1117/12.3004874

11 October 2023 Research on traffic flow prediction based on spatio-temporal convolutional networks with attention mechanism

Hong Zhang, Tian-xin Zhao, Jie Cao, Su-nan Kan

Author Affiliations +

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128002M (2023) https://doi.org/10.1117/12.3004874
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Traffic flow prediction is a key technology in intelligent transportation systems and plays a crucial role in intelligent transportation. How to obtain dynamically changing spatial relationships of road networks without prior knowledge is a challenge in current traffic flow prediction. In response to the spatiotemporal dynamic changes of traffic flow, this paper proposes a traffic flow prediction method ASTCNN based on attention and time convolutional networks. This method utilizes time convolution to extract long-term dependencies in traffic flow time series; Based on an adaptive node embedding attention mechanism, model the dynamic correlation of different road network nodes without requiring prior knowledge of the graph; At the same time, by stacking time convolution and attention network layers, dynamic spatial correlation and nonlinear time dependence at different time levels can be effectively excavated, enhancing the representation ability of the model's spatiotemporal modeling. The experimental results on two real traffic datasets have shown that this method is concise and effective, and its predictive performance is superior to common baseline models.

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

Citation Download Citation

Hong Zhang, Tian-xin Zhao, Jie Cao, and Su-nan Kan "Research on traffic flow prediction based on spatio-temporal convolutional networks with attention mechanism", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128002M (11 October 2023); https://doi.org/10.1117/12.3004874

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