## 5 Result and Analysis
### Performance Comparison
#### 5.2.2 Performance Comparison on Testing Dataset  
Table 2 presents the hourly road-wise accuracy performance of our proposed model and four other state-of-the-art neural networks on the testing dataset from December 3 to December 10, between 08:00 to 11:00. Our proposed model outperforms other state-of-the-art models by a large margin. Specifically, for the 10-min prediction horizon, our proposed RHRNet demonstrates an average gain of approximately 4% compared to the next best model, PredNet and an average gain of 6-15% compared to other methods. For 30-min prediction horizons, our proposed model achieves an increment in accuracy of 2-13% compared to other compared models. Similarly, for the 60-min prediction horizons, our proposed model attains an average improvement of 1.5-15% compared to other state-of-the-art models. Figure 4 presents a comparison of road-wise prediction accuracy on 3 December 2019 between 08:00 to 12:00, at 5-min intervals. The proposed RHRNet model is compared to other state-of-the-art models such as PredNet, UNet, ConvLSTM, and Autoencoder, and with the prediction baseline. Figure 4a shows the road-wise accuracy comparison at a prediction horizon of 10 min. From the line chart, we can see that the proposed RHRNet achieves higher accuracy compared to all other models for almost all the testing timeline, reaching the highest value of 95% at 11:15, followed by the PredNet. The UNet and ConvLSTM have much lower performance that RHRNet but have similar performance to one another, while the Autoencoder performance is closer to the baseline. Similarly, Figure 4b shows the road-wise accuracy comparison at a prediction horizon of 30 min. This chart shows similar trends as in Figure 4a, with RHRNet achieving better accuracy followed by PredNet. The UNet and ConvLSTM show similar performance but are lower than the proposed model, while Autoencoder performance is much closer to the baseline. Table 2 and Figure 4 demonstrate that the RHRNet performs better in terms of accuracy than other state-of-the-art traffic congestion prediction models. However, it is uncertain whether the trained model can represent other congestion levels better. To address this, Figure 5 presents a comparison of precision and recall metrics on the testing dataset with respect to PredNet, UNet, ConvLSTM, and Auto-encoder. Quartiles Q1, Q2, and Q3 are shown in the Figure 5, with the mean value indicated by a cross, and the range of the data distribution shown by lines above and below the quartiles. Figure 5a shows the precision and recall comparison for 10-min prediction horizons. All three congestion levels, namely \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline
**P.H** & \multicolumn{4}{c}{**Precision**} & \multicolumn{4}{c}{**Recall**} & \multicolumn{4}{c}{**Accuracy**} \\ \hline  & R.H & P.N & U.N & C.L & A.E & R.H & P.N & U.N & C.L & A.E & R.H & P.N & U.N & C.L & A.E \\ \hline
10 & **0.898** & 0.872 & 0.844 & 0.836 & 0.766 & **0.887** & 0.857 & 0.826 & 0.812 & 0.726 & **0.928** & 0.884 & 0.862 & 0.857 & 0.772 \\
30 & **0.866** & 0.849 & 0.822 & 0.821 & 0.719 & **0.871** & 0.853 & 0.797 & 0.810 & 0.712 & **0.886** & 0.860 & 0.847 & 0.839 & 0.754 \\
60 & **0.856** & 0.847 & 0.804 & 0.746 & 0.730 & **0.853** & 0.846 & 0.701 & 0.731 & 0.705 & **0.867** & 0.842 & 0.821 & 0.757 & 0.749 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance comparison on training dataset for the prediction horizons of 10, 30, and 60 min. The best result is marked in bold. Jam, Slow, and Free, demonstrate significantly higher performance in both precision and recall metrics, followed by PredNet. The prediction distribution range for the proposed RHRNet is smaller compared to all other models, with the autoencoder having the largest range. Similar results can be seen for 30-min and 60-min prediction horizons in Figure 5b,c, respectively. In both cases, the proposed RHRNet shows higher precision and recall results, followed by the next best model, PredNet.