Traffic Flow Prediction with Random Walks on Graph and Spatiotemporal Bidirectional Attention Transformer
Shudong Yang,
Yimin Zhou,
Zhengbin Wu
Abstract:Traffic flow prediction is crucial in intelligent transportation systems. Considering the severe disruptions caused by traffic accidents or congestion, a time series model is developed for traffic flow prediction based on multiple random walks on graphs (MRWG) and the bidirectional spatiotemporal attention mechanism (BSAM), which can adapt to both normal and exceptional situations. The MRWG mechanism is applied to capture spatial features of urban areas during traffic accidents and congestion, especially the s… Show more
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