A Dynamic Traffic Community Prediction Model Based on Hierarchical Graph Attention Network

The time-varying property of traffic networks has brought a problem of modeling large-scale dynamic networks. Based on the real-time traffic sensing data of the road network, community division and prediction can effectively reduce the complexity of local management in urban regions. However, traffic-based communities have complex topology and real-time dynamic features, and traditional community division and topology prediction cannot effectively be applied to this structure. Therefore, we propose a dynamic traffic community prediction model based on hierarchical graph attention network. It uses the hierarchical features fusion with spatiotemporal convolution and the ADGCN proposed in this paper to compose a hierarchical graph attention architecture. In which, each layer component coordinates to perform different features extraction for capturing traffic community of road network in different time periods respectively. Finally, the output features of each layer are combined to represent the dynamically divided regions in the traffic network. The effectiveness of the model was verified in experiments on the Xi'an urban traffic dataset.