Graph Transformer-Based Dynamic Edge Interaction Encoding for Traffic Prediction

doi:10.1109/TITS.2024.3513325

Graph Transformer-Based Dynamic Edge Interaction Encoding for Traffic Prediction

2024-01-01

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DOI：10.1109/TITS.2024.3513325

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Traffic prediction is an essential function of intelligent transportation system for traffic control and autonomous driving. Most existing methods encode traffic spatial and temporal data separately, and then design a feature fusion module to correlate spatial and temporal features. However, spatial information is often static, and repetitive static spatial encoding leads to waste of resources, especially in large-scale traffic network prediction. In this paper, we propose a dynamic edge interaction encoding method for spatio-temporal features based on inverse Transformer (iTransformer) and Graph Transformer, named iTPGT-former. The dynamic edge interaction process is designed to embed dynamic temporal features into static edges via a convolutional embedding module. To enhance the Graph Transformer, a relative position encoding strategy based on the self-attentive score of the positive definite kernel (PDK) on graphs and a method for graph substructure encoding (GSE) via enumeration of paths are introduced. In the experimental and discussion session, the iTPGT-former is considered for accuracy, parameters, inference speed, and rich ablation experiments are provided based on six publicly available traffic datasets. The results show that iTPGT-former outperforms the baseline model in both traffic flow and traffic speed prediction. The maximum improvement is achieved in the METR-LA 60-min speed prediction task, with 15.2% reduction in Mean Absolute Percentage Error (MAPE). In addition, the inference of iTPGT-former is significantly faster than the GCN-based method. Our implementation of the iTPGT-former is available at https://github.com/ouyangnann/iTPGTN-former.

Keywords:

Transformers

Feature extraction

Encoding

Predictive models

Accuracy

Roads

Time series analysis

Data models

Data mining

Attention mechanisms

Traffic prediction

graph transformer

inverse transformer

spatio-temporal interactive encoding

Graph Transformer-Based Dynamic Edge Interaction Encoding for Traffic Prediction

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