ByGCN: Spatial Temporal Byroad-Aware Graph Convolution Network for Traffic Flow Prediction in Road Networks

As a fundamental technology in intelligent transportation systems (ITS), accurate traffic flow prediction has emerged as a critical challenge in real-time applications. How to fully utilize the traffic data, and capture the spatial temporal correlation are keys to improve the model's prediction ability. Numerous neural networks have been proposed to address this issue. However, most of these existing methods have the following two problems: 1) Lack of byroads information. Meaning that the existing methods do not consider the byroads in real-life traffic environments; 2) Lack of potential learning ability. Meaning that the existing methods suffer the non-similar forgetting and hard to gain the multi-hop correlation. To overcome these problems, we propose a novel Spatial Temporal <u>By</u>road-Aware <u>G</u>raph <u>C</u>onvolution <u>N</u>etwork (ByGCN) in this paper. ByGCN consists of byroad identification and spatial temporal learning modules. In the first module, we design spatial temporal decoupling and graph diffusion blocks to identify the byroads and reconstruct them into the flow data. In the second module, with the help of spatial temporal attention and GCN, our module can capture the complex spatial temporal correlation. Experiments on four real-world traffic datasets demonstrate that ByGCN outperforms the state-of-the-art methods.