CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13160 > Article
Paper
16 May 2024 Short-term traffic flow prediction of coastal urban road nodes based on A3-TGCN
Sijie Xu, Junting Ou, Lirong Huang, Zhongjie Zhou, Hongbo Sun
Author Affiliations +
Proceedings Volume 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024); 1316011 (2024) https://doi.org/10.1117/12.3030595
Event: 4th International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 2024, Beijin, China
Abstract
In recent years, with the improvement in living standards and economic development, the number of cars in the city has been increasing year by year. This has put pressure on the carrying capacity of the roads and led to traffic congestion. However, it is difficult for traditional time series models to predict future traffic flows accurately. In this paper, we take the traffic data of the coastal urban area as our research object, combining spatial and temporal factors of the city. We observed the excellent performance of the improved model in complex environments by the attention temporal graph convolutional network(A3T-GCN) model. The root mean square error(RMSE) value is 33.305, the mean absolute error (MAE) value is 21.257, and the variance(var) is 90.3%. This study provides a practical algorithmic foundation for intelligent transport. The A3T-GCN algorithm, which is based on the self-attention mechanism, has a significant advantage in traffic flow prediction, especially when there is limited data
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Sijie Xu, Junting Ou, Lirong Huang, Zhongjie Zhou, and Hongbo Sun "Short-term traffic flow prediction of coastal urban road nodes based on A3-TGCN", Proc. SPIE 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 1316011 (16 May 2024); https://doi.org/10.1117/12.3030595
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 7 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Roads
Data modeling
Machine learning
Process modeling
Visualization
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top