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Graph Spatial-Temporal Transformer Network for Traffic Prediction
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AI
DOI：10.1016/j.bdr.2024.100427.png)
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Abstract
En 中文
Traffic information can reflect the operating status of a city, and accurate traffic forecasting is critical in intelligent transportation systems (ITS) and urban planning. However, traffic information has complex nonlinearity and dynamic spatial -temporal dependencies due to human mobility, bringing new traffic forecasting challenges. This paper proposed a graph spatial -temporal transformer network for traffic prediction (GSTTN) to cope with the above problems. Specifically, the proposed framework explores spatial characteristics of the acrossroad network of traffic information hidden in human behavior patterns via a multi -view graph convolutional network (GCN). Furthermore, the transformer network with a multi -head attention mechanism is adopted to capture the random disturbance in the time series characteristics of traffic information. As a result, these two components can be used to model spatial relations and temporal trends. Finally, we examine real -world datasets, and the experiments show that the proposed framework outperforms the current state-of-the-art baselines.
Keywords:
Spatial-temporal dependencies
Graph convolutional network
Transformer networks
Traffic prediction
Journal
IF:
4.2
Papers:
406
Citations:
1.1K