1. A two-stage convolution network algorithm for predicting traffic speed based on multi-feature attention mechanisms.
- Author
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Wang, Chia-Hung, Cai, Jiongbiao, Ye, Qing, Suo, Yifan, Lin, Shengming, and Yuan, Jinchen
- Subjects
TRAFFIC speed ,DEEP learning ,RECURRENT neural networks - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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