| Home | KSAE | E-Submission | Sitemap | Contact Us |  
top_img
International Journal of Automotive Technology > Volume 24(4); 2023 > Article
International Journal of Automotive Technology 2023;24(4): 1013-1023.
doi: https://doi.org/10.1007/s12239-023-0083-9
TRAFFIC FLOW FORECASTING OF GRAPH CONVOLUTIONAL NETWORK BASED ON SPATIO-TEMPORAL ATTENTION MECHANISM
Hong Zhang , Linlong Chen , Jie Cao, Xijun Zhang , Sunan Kan , Tianxin Zhao
College of Computer & Communication, Lanzhou University of Technology
PDF Links Corresponding Author.  Hong Zhang  , Email. zhanghong@lut.edu.cn
ABSTRACT
Accurate traffic flow forecasting is a prerequisite guarantee for the realization of intelligent transportation. Due to the complex time and space features of traffic flow, its forecasting has always been a research hotspot in this field. Aiming at the difficulty of capturing and modelling the temporal and spatial correlation and dynamic features of traffic flow, this paper proposes a novel graph convolutional network traffic flow forecasting model (STAGCN) based on the temporal and spatial attention mechanism. STAGCN model is mainly composed of three modules: Spatio-temporal Attention (STA-Block), Graph Convolutional Network (GCN) and Standard Convolutional Network (CN), model the periodicity, spatial correlation and time dependence of traffic flow respectively. STA-Block module models the spatio-temporal correlation between different time steps through the spatio-temporal attention mechanism and gating fusion mechanism, and uses GCN and CN to capture the spatial and temporal features of traffic flow respectively. Finally, the output of the three components is predicted through a gated fusion mechanism. A large number of experiments have been conducted on two data sets of PeMS. The experimental results demonstrate that compared with the baseline method, the STAGCN model proposed in this paper has better forecasting performance.
Key Words: Traffic flow forecasting, Spatio-temporal attention mechanism, Graph convolutional network, Spatio- temporal correlation, Gated fusion mechanism
TOOLS
Preview  Preview
Full text via DOI  Full text via DOI
Download Citation  Download Citation
  Print
Share:      
METRICS
0
Scopus
423
View
26
Download
Related article
Editorial Office
21 Teheran-ro 52-gil, Gangnam-gu, Seoul 06212, Korea
TEL: +82-2-564-3971   FAX: +82-2-564-3973   E-mail: manage@ksae.org
About |  Browse Articles |  Current Issue |  For Authors and Reviewers
Copyright © The Korean Society of Automotive Engineers.                 Developed in M2PI
Close layer
prev next