Abstract:

Aiming at the problem that existing prediction models fails to fully utilize the spatio-temporal correlation of traffic flow data， this paper proposes a deep learning model that combines clustering algorithm， graph neural network （GNN） and gated recurrent unit （GRU）. First， the algorithm classifies to classifies preprocessed data into traffic patterns； then， the GNN is used to extract the spatial correlation of the traffic flow of the complex road network， integrating Pearson correlation analysis of the roads and the local clustering coefficients of the nodes to uncover potential node connections； the GRU is used to extract the temporal correlation between the traffic flow data， and through the mechanism of self-attention， captures interdependencies among data； finally， the outputs of GRU and GNN are combined with original inputs via residual connectivity， and the final prediction results are obtained after the fully connected layer. Multiple sets of experimental results demonstrate the superior prediction accuracy of the proposed model is over other baseline models and contrast model.

Key words: traffic flow prediction, graph neural network, clustering algorithm, gated recurrent unit, Pearson correlation coefficient, local clustering coefficient