Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure

Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic graph convolutional network model (Res-DGCN) based on the residual network structure for crowd inflow and outflow prediction in urban areas. Firstly, as the attention layer, the spatio-temporal attention module (SA) is employed for capturing the spatial relationship between the target node and the multi-order adjacent nodes by processing the features of the human flow data. Secondly, a conditional convolution module (SCondConv) is used to enhance the model's capacity for learning about the shifting characteristics of crowd flow to obtain spatial dependence. Finally, we train the model with the Huber loss function to lower the model's sensitivity to outliers and achieve optimal convergence. In two public datasets, the mean absolute error (MAE) of the proposed model is improved by 5.2% and 9.4%, respectively, compared to the baseline models, and the root mean square error (RMSE) is improved by 4.8% and 8.8%, confirming the model's usefulness for crowd flow prediction tasks.

Automated summary

This research proposes a dynamic graph convolutional network model (Res-DGCN) based on the residual network structure for accurate crowd flow prediction in urban areas. The model utilizes the spatio-temporal attention module (SA) to capture the spatial relationship between target nodes and adjacent nodes, and the conditional convolution module (SCondConv) to learn the shifting characteristics of crowd flow. The proposed model achieves better performance compared to baseline models, with improvements in mean absolute error (MAE) and root mean square error (RMSE).