Affiliations: [a]
College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou City, Fujian Province, China
| [b] Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou City, Fujian Province, China
Correspondence:
[*]
Corresponding author. Prof. Chia-Hung Wang, Fujian University of Technology, Fuzhou 350118, China. E-mail: [email protected].
Abstract: In recent years, it has been shown that deep learning methods have excellent performance in establishing spatio-temporal correlations for traffic speed prediction. However, due to the complexity of deep learning models, most of them use only short-term historical data in the time dimension, which limits their effectiveness in handling long-term information. We propose a new model, the Multi-feature Two-stage Attention Convolution Network (MTA-CN), to address this issue. The MTA-CN intercepts longer single-feature historical data, converts them into shorter multi-feature data with multiple time period features, and uses the most recent past point as the main feature. Furthermore, two-stage attention mechanisms are introduced to capture the importance of different time period features and time steps, and a Temporal Graph Convolutional Network (T-GCN) is used instead of traditional recurrent neural networks. Experimental results on both the Los Angeles Expressway (Los-loop) and Shen-zhen Luohu District Taxi (Sz-taxi) datasets demonstrate that the proposed model outperforms several baseline models in terms of prediction accuracy.
