A Spatio-Temporal Graph Convolutional Network Integrating Multi-Source Heterogeneous Data for Traffic Flow Prediction

In traffic flow prediction, many external factors, such as weather and holiday factors, have a significant impact on traffic flow. However, previous research on traffic flow prediction mainly focused on the internal correlations within traffic flow data, failing to integrate or comprehensively integrate the external factors affecting traffic flow to build prediction models. Based on this, this study collected as comprehensively as possible the data of external factors affecting traffic flow and integrated these data to construct a traffic flow prediction model. This study proposes a Temporal Graph Convolutional Network algorithm (IM - GCN), which comprehensively integrates multi - source heterogeneous external factors that affect traffic flow and for which collectable data are available, for traffic flow prediction modeling. This algorithm expands the receptive field of traffic flow features and enhances the reference weight of data with stronger correlations. The effectiveness of the proposed algorithm was verified through a large number of experiments on widely used traffic flow datasets. Experiments show that, compared with the state - of - the - art algorithms, on the premise that the increase in complexity reflected by the computational time consumption is less than 1%, the accuracy of the prediction model has been improved by 1% - 4%. Ablation experiments experimentally prove that the external factors affecting traffic flow added in this study can effectively improve the performance of the traffic flow prediction model.