## 5 Discussion  
Traffic is highly dynamic, so building an efficient traffic prediction model is challenging. Further, the robustness of a model is greatly dependent on capturing both spatial and temporal features of traffic. For the SVR model, it was particularly challenging to capture those features because it is less capable of dealing with a vast amount of data and noisy data samples, specifically when overlapping of target classes occurs. The obtained experimental results demonstrate its incapability of dealing with these problems. On the other hand, the LSTM and GRU models can remember relevant past information because of their memory characteristics. Hence, these models are suitable for time series prediction tasks. However, these models require feeding the data sequences directly to the input, and the path distances will always increase proportionally, which can cause inefficiency in capturing long-range correlation relationships. On the contrary, convolution layers can learn the hidden features of data more effectively than the other baselines. Hence, instead of feeding data directly to the model, 1D convolution layers were used to extract features first, and then the goodness of the LSTM or GRU models was employed to process those learnt features sequence-by-sequence for prediction purposes. This technique demonstrated very encouraging performance improvements in terms of the used state-of-the-art metrics. During the current study, different state-of-the-art techniques were assessed for data normalisation; mainly, MaxAbsScaler--scaling each feature by its maximum absolute value; MinMaxScaler--transforming features by scaling each feature to a given range 0, 1; RobustSCaler--scaling features using statistics that are robust to outliers; and StandardScaler--standardise elements by removing the mean and scaling to unit variance. Interestingly, MaxAbsScaler and StandardScaler exhibited the best and most minor outcomes, respectively. This study also assessed the Bi-Directional LSTM and GRU architecture instead of normal LSTM or GRU. Interestingly, in terms of the MAPE, the proposed model had \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **SVR** & **LSTM** & **GRU** & **CNN–GRU** & **CNN–LSTM** \\ \hline MAPE & 9.30\% & 8.27\% & 8.31\% & 8.897\% & 7.27\% * \\ RMSE & 0.78 & 0.0291 & 0.0288 & 0.0295 & 0.0266 * \\ MAE & 1.09 & 0.02178 & 0.02042 & 0.02061 & 0.01896 * \\ \(R^{2}\) & 0.784 & 0.896 & 0.898 & 0.894 & 0.913 * \\ \hline \hline \end{tabular} Here, * represents the best found result among the models under comparison.  
\end{table} 
Table 3: Performance comparison of the different models under study in terms of the used state-of-the-art metrics on the traffic speed dataset. Figure 9: Prediction performance comparison among the different models under study over a week horizon on the traffic speed dataset. a performance degradation of 60.9%. Moreover, batch normalisation layers, which are additional layers used to normalise the outputs of the first hidden layer before passing them on to the next hidden layer and aim to keep the network stable throughout training, were inserted into the proposed model. Further, the insertion of layer normalisation within the hidden layers was considered to predict the normalisation statistics from the aggregated inputs of the neurons and to resist any additional dependencies among the training samples. Both exhibited discouraging results, particularly the insertion of the normalisation layers. Although some previous research, for example [37], had opposite findings. However, in the current type of data, layer normalisation within a hidden layer failed to correctly predict the normalisation statistics from the aggregated inputs of the neurons. In this study, the proposed model was trained for different combinations of 1D-CNN and LSTM layers using the same environment and identical hyper-parameters. Table 4 indicates the performance of the proposed model in terms of different parameters.