# A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
## 5 Experiment Analysis
### Experimental Results  
In order to better understand the prediction quality, this section uses an OD matrix heatmap to visualize the prediction results of a certain time slice. The rows represent the source station numbers, and the list represents the destination station numbers. Each grid represents the number of OD flows, and the larger the OD flow, the deeper the value. The visualization results show that only a few OD values are not zero, and the high sparsity of OD flows increases the difficulty of model prediction. By comparing and analyzing the real values of predicted time slices through OD matrix heatmap visualization, as shown in Figure 12, it was found that although the proposed model had some prediction errors, it fitted well to the overall trend, indicating that the model can capture the spatiotemporal correlation of OD between highway stations well.  
Figure 13 presents the experimental results of different models of OD flow between highway stations in Sichuan Province. The analysis results show that the performance evaluation of the model proposed in this paper is better in the experiment, indicating the effectiveness of the model construction and feature selection in this study. Specifically, in terms of performance indicator RMSE, the model proposed in this chapter has improved by 16.41%, 10.39%, 12.99%, and 6.95%, respectively, compared to ConvLSTM, ST-GCN, ST-RESNET, and ASTGCN. In terms of performance indicator MAE, the model proposed in this chapter has improved by 18.82%, 11.10%, 12.7%, and 5.71%, respectively, compared to ConvLSTM, ST-GCN, ST-RESNET, and ASTGCN. In terms of performance indicator CPC, the model proposed in this chapter has improved by 17.95%, 11.05%, 15.21%, and 5.33%, respectively, compared to ConvLSTM, ST-GCN, ST-RESNET, and ASTGCN.  
Figure 11: Hyperparameter impact analysis.  
Through further analysis of the above experimental results, the following conclusions can be drawn.  
(1) In the selected benchmark model, ConvLSTM performs relatively weaker than other models, mainly because CNN uses convolution operations instead of LSTM's fully connected layers to extract spatial features, but its ability to capture spatial correlations is weaker than GCN.  
(2) Although the benchmark model ST-RESNT introduces residual connections and dense connection blocks to enhance feature extraction capabilities, and the ST_GCN constructs a graph neural network model that processes spatiotemporal data to capture spatiotemporal correlations, both models can capture spatiotemporal features of spatiotemporal data. However, the benchmark model ST-GCN performs better in traffic flow prediction tasks.  
(3) The benchmark model ASTGCN combines attention mechanism and graph neural network, and enhances the model's ability to capture important information by introducing attention mechanism, resulting in better performance than other benchmark models.  
(4) The model proposed in this chapter integrates a heterogeneous deep learning model consisting of graph embedding representation learning (node2 vec), gated recurrent unit network (GRU), and graph attention network (GAT). It also utilizes multi-task learning (MLT) to introduce constraints between tasks through parameter sharing, considering more  
Figure 12: Comparison between predicted and truth values of OD matrix in a time slot.  
Figure 13: Comparison of performance evaluation results between the proposed model and the benchmark model.  
details in capturing spatiotemporal correlations, thus improving the predictive performance of the model.