## 5 Results
### Experimental Results  
The performance results are compared in Table 2. We compared our results on the Porto and Manhattan datasets with the previous state-of-the-art baselines models. Our model outperforms the state-of-the-art models LSTM(BOC+W2V) and ST-GCN by a large margin. We modeled with dynamic spatial and temporal dependencies, on both of which our model consistently outperforms the previous models. Our model performance is efficient and achieves long-range temporal dependencies and hidden spatial dependencies effectively. We also observed that the performance of our model is much better than NN and simple MMLP. As shown in Table 2, NN and MMLP performances are worst for calculating the error distance score in kilometers. On the Porto dataset, our performance is 45-47% better than NN and MMLP and similar to the improved variant of MMLP-SEQ. On the Manhattan dataset, our performance is 35-40% better than NN and MMLP and the same as MMLP-SEQ. In our approach, we represent the input features constituted by the driver and time. The spatial zone embedding is the coordinates of each zone cluster fed into the embedding layer. The ARIMA model results are the worst of all baseline models.  
The previous approach, FC-LSTM, and the improved variant with BOC and W2V, perform well when combined with LSTM as LSTM(BOC+W2V). The error rate of the improved version of LSTM(BOC+W2V) is lower. In the LSTM(BOC+W2V) approach, BOC and W2V indicate the recurrent neural network with features. The ST-GCN is outperformed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**City DataSet** & **Batch Size** & **Learning Rate** & **Neurons** & **Optimizer** & **Activation Function** & **Training Time (M)** & **Testing Time (S)** \\ \hline Porto & 16 & \(10^{-3}\) & 256 & Adam & ReLU & 74 & 0.23 \\ Manhattan & 16 & \(10^{-3}\) & 256 & Adam & ReLU & 69 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter setting and training summary.  
by LSTM(BOC+W2V). The error rate more improved compared to the previous models. The transformer approach is new; being first used in 2017. The transformer encoder and decoder perform NLP translation. On the Porto dataset, we compared the performance of our approach with LSTM(BOC+W2V) and ST-GCN. Our approach performs 30-35% better than the approaches mentioned above. On the Manhattan dataset, we obtained 25-32% better results. Figure 8 illustrates the error distance score on Manhattan and Porto datasets. In Figures 9 and 10, we compare DWSTTNs in regression and multi-class classification settings, respectively. We removed two output neurons and used categorical cross-entropy as a loss function during the training of classification models. In this way, the positions of the locations were restricted to the list of centroids for clusters. However, through the corresponding probability coming from the SoftMax layer, the coordinates were weighted by each cluster centroid. The experimental results show the exact location by applying the regression procedure on the longitude and latitude values. The rate of EDS decreased on the Manhattan dataset, demonstrating the ability of the proposed method to perform excellently on cities not equally extended in longitude and