A generalized feature projection scheme for multi-step traffic forecasting

Zeb，Adnan1; Zhang，Shiyao1,2; Wei，Xuetao1; Yu，James Jianqiao1,3

2024-06-15

10.1016/j.eswa.2023.122962

Expert Systems with Applications   影响因子和分区

0957-4174

Exploiting spatial–temporal correlations has long been regarded as the cornerstone of traffic state prediction. Among existing techniques, temporal graph neural networks (TGNNs) have recently emerged as a prominent solution for modeling complex spatial–temporal traffic data correlations. Existing studies on TGNNs mainly focus on developing new building blocks to embed hidden correlations into a unified latent representation, which is mapped to predictions of distinct horizons. However, mapping the same latent features to distinct scalar predictions makes the gradient computation challenging for updating model parameters in the relevant directions. Besides, TGNNs are biased towards the shared temporal patterns while neglecting the complex dependencies within each data series, which can be captured to enrich latent features. To handle these problems jointly, we propose a novel feature projection scheme for the traffic prediction framework of TGNNs. The proposed projection scheme is based on spatial convolutions that first generate horizon-specific feature maps and then transform them into scalar predictions of the corresponding horizons. These horizon-specific feature maps establish interactions between the unified latent representation and the corresponding output values to bring the predictions closer to the true values. Besides, the proposed scheme also serves as a pattern modeling phase that enhances the expressivity of TGNNs by enriching latent features with data source-wise patterns of distinct time steps. Comprehensive experiments on two real-world traffic datasets demonstrate that the proposed scheme enhances the predictive performance and reduces the model parameters of TGNNs.

Feature projection Intelligent transportation systems Spatial transformations Traffic forecasting

SCI

英语

第一 ; 通讯

ENGINEERING

2-s2.0-85180538725

Scopus

1.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Research Institute for Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen,518055,China
3.Department of Computer Science,University of York,York,YO10 5DD,United Kingdom

Zeb，Adnan,Zhang，Shiyao,Wei，Xuetao,et al. A generalized feature projection scheme for multi-step traffic forecasting[J]. Expert Systems with Applications,2024,244.

Zeb，Adnan,Zhang，Shiyao,Wei，Xuetao,&Yu，James Jianqiao.(2024).A generalized feature projection scheme for multi-step traffic forecasting.Expert Systems with Applications,244.

Zeb，Adnan,et al."A generalized feature projection scheme for multi-step traffic forecasting".Expert Systems with Applications 244(2024).