# A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency
## 5 Experimental Results and Analysis
### The Prediction Performance of OD Evacuation Volume and Background Load  
In the experiments, we further verify the effect of hierarchy features on the prediction performance of existing methods. We compare the performance of hierarchy partition with grid partition using 12 h as the historical time window for training, as shown in Figure 5.  
The target is to predict potential evacuation demands for the next 1 h, 6 h, and 12 h. With increasing iterations of training, the RMSE becomes stable after 500 iterations. In general, the length of the prediction window has a direct impact on prediction performance. From Figure 5a, it can be seen that the model is best trained with a 1 h prediction window. In the first 500 iterations, the validation RME reached a minimum of 5.012. By extending the prediction scope to 12 h, the RMSE for three curves increases significantly, indicating that the prediction performance of the long-term prediction is lower than that of the short-term, which is more applicable to the need for short-term prediction in emergency scenarios. The small time scope allows the model to perform well, but the length of capturing the time series features is limited. In each subgraph, the overall RMSE under hierarchy partition is lower than that under grid partition, with a minimum value of 5.165 during 500 iterations. This is due to the fact that grid partition does not handle uneven distribution well, and using equal grids to aggregate OD data leads to a polarization in the dense and sparse data. This results in an over-generalized amount of OD evacuation after grid partition, and the evacuation features from one grid to another are not well-reflected. Therefore, the training performance of the traffic prediction under hierarchy partition is better than that of the grid partition.  
In terms of background load prediction, the same 12 h time window is used to predict the background load for the next 1, 6, and 12 h, matching the dynamic OD evacuation volume. The experiments compare our proposed HCN with general graph convolution methods, respectively. The HC-LSTM employs spatial feature aggregation with HCN in each LSTM cell. Figure 6 shows the performance of GCN, HCN, and HC-LSTM on the  
Figure 5: The prediction error of OD evacuation volume based on hierarchy and grid partitions.  
validation data. As the iterations increased in Figure 6, the RMSE under different modules leveled off gradually, and HC-LSTM outperformed the GCN and HCN in three prediction periods. In comparison to the baseline GCN, the average RMSE of HCN increases by 35.76%, while the HC-LSTM average RMSE increases by 48.28%. And HCLSTM uses spatio-temporal convolution, which improves by 19.49% relative to spatial convolution HCN. Similarly, the prediction performance of the models decreases as the scope of prediction window increases. The RMSE of HC-LSTM reaches a minimum of 26.63 for the next 1 h prediction. The experiments verified that the HCN outperforms the GCN due to the addition of hierarchy features that improve the fitting ability to traffic spatial relation.  
Therefore, by obtaining historical ODs and background load data of the traffic network, it is possible to make prediction for future traffic loads. Also, the experiments demonstrate that the performance of traffic prediction can be improved by mining the hierarchical semantic information of the road network.