Ming Luo1, Huili Dou2, Ning Zheng3,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 265-282, 2024, DOI:10.32604/cmc.2023.040067
- 30 January 2024
Abstract Traffic prediction already plays a significant role in applications like traffic planning and urban management, but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data. As well as to fulfil both long-term and short-term prediction objectives, a better representation of the temporal dependency and global spatial correlation of traffic data is needed. In order to do this, the Spatiotemporal Graph Neural Network (S-GNN) is proposed in this research as a method for traffic prediction. The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations… More >