# Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
## 4 Results Analysis and Discussion
### Ablation Study  
We performed an ablation study of the proposed model to investigate the causality of the model. The results are shown in Table 4. The experimental evaluations demonstrated that the combination of the AttBDLSTM block features and the FCN block features improved the model's performance. We performed an ablation study on three different time lags, e.g., 3, 6, and 12 (15, 30, and 60 min, respectively), and we obtained surprising results by augmenting two models. When the time lag was set as 3 (e.g., 15 min), FCN, BDLSTM, AttBDLSTM, and the proposed model had MAE and MAPE values of 6.73, 18.10%, 1.40, 2.99%, 1.29, 2.77%, and 1.17, 2.73%, respectively. When the time lag was set as 6 (e.g., 30 min), the MAE and MAPE of the FCN, BDLSTM, AttBDLSTM, and the proposed model were 6.58, 18.99%, 1.42, 3.11%, 1.34, 2.99%, and 1.19, 2.75%, respectively. When the time lag was set as 12 (e.g., 60 min), we obtained the MAE and MAPE of the compared models as follows: 8.09, 20.48%, 1.54, 3.41%, 1.39, 3.11%, and 1.30, 2.99%, respectively. After the ablation study, based on our experimental findings, we can see that the attention mechanism along with BDLSTM can help to improve the performance of the proposed model. Moreover, we can conclude that the FCN block augmented with the AttBDLSTM block statistically helped improve the overall model performance.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Time Lag** & **Error** & **FCN** & **BDLSTM** & **AttBDLSTM** & **Proposed Model** \\ \hline
3 & MAE & 6.73 & 1.40 & 1.29 & 1.17 \\  & MAPE & 18.10\% & 2.99\% & 2.77\% & 2.73\% \\ \hline
6 & MAE & 6.58 & 1.42 & 1.34 & 1.19 \\  & MAPE & 18.9\% & 3.11\% & 2.99\% & 2.75\% \\ \hline
12 & MAE & 8.09 & 1.54 & 1.39 & 1.30 \\  & MAPE & 20.48\% & 3.41\% & 3.11\% & 2.99\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Time Lags** & **Models** & **Error** & **T + 1** & **T + 3** & **T + 6** & **T + 12** \\ \hline  & FCN & MAPE & 18.10 & 16.04 & 16.72 & 20.30 \\  & GRU & MAPE & 3.82 & 5.48 & 7.52 & 7.24 \\  & LSTM & MAPE & 4.22 & 5.87 & 6.35 & 6.23 \\
3 & LSTM-DNN & MAPE & 4.45 & 5.59 & 5.56 & 6.97 \\  & LSTM-FCN & MAPE & 3.62 & 5.18 & 5.29 & 5.59 \\  & BDLSTM & MAPE & 2.99 & 4.99 & 5.26 & 5.15 \\  & PROPOSED-MODEL & MAPE & 2.73 & 4.95 & 5.15 & 5.05 \\ \hline  & FCN & MAPE & 18.99 & 16.26 & 17.14 & 21.87 \\  & GRU & MAPE & 3.69 & 6.52 & 8.88 & 8.64 \\  & LSTM & MAPE & 3.90 & 6.96 & 7.53 & 6.28 \\
6 & LSTM-DNN & MAPE & 5.0 & 7.09 & 7.08 & 6.33 \\  & LSTM-FCN & MAPE & 3.40 & 6.91 & 6.81 & 5.96 \\  & BDLSTM & MAPE & 3.11 & 6.60 & 6.74 & 6.09 \\  & PROPOSED-MODEL & MAPE & 2.75 & 5.39 & 6.66 & 5.80 \\ \hline  & FCN & MAPE & 20.48 & 17.01 & 21.18 & 27.89 \\  & GRU & MAPE & 4.37 & 9.06 & 10.04 & 8.53 \\  & LSTM & MAPE & 5.13 & 9.36 & 9.45 & 7.53 \\
12 & LSTM-DNN & MAPE & 6.63 & 10.07 & 9.52 & 7.80 \\  & LSTM-FCN & MAPE & 4.02 & 9.78 & 9.52 & 6.98 \\  & BDLSTM & MAPE & 3.41 & 8.93 & 9.25 & 7.20 \\  & PROPOSED-MODEL & MAPE & 2.99 & 7.52 & 9.05 & 6.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with baseline models(MAPE).