## 4 Results and Discussion
### Muting of All Vehicles Except Target
#### 4.1.3. Summary of Muting Effects
When assessing the ETA predictions, the results are different, as in the eastbound direction the full model performed best Figure 12, whereas in the westbound direction the headway limited model performed best Figure 13. This is an interesting finding, as the data contribution is different for the two directions. The eastbound direction contains, in addition to the target itself, line number 5 as the most common line (Figure 9). In the westbound direction, the most common line is line 17 (non-target vehicles). This seems to be reflected in the ETA prediction, where the headway limited model performs best in the scenario where the most common data come from line 17 (westbound), whereas in the case where the most common data come from a different line (line 5) the full model performs better Figure 9). Why this is not found in the trajectory prediction can be explained by the fact that in a setting where other lines contribute more to the data, this can be leveraged by the ETA prediction because intuitively the progress through the network of other lines might affect the final arrival time. In contrast, trajectory prediction is a short-term prediction for the next 40 s only, where the overall network state might be less important. For this example, vehicles immediately ahead of the target will be the most important to make an accurate prediction. This highlights the importance of designing not only a custom method for each line but also for each prediction target. Figure 11: The model was muted to the headway meaning only vehicles ahead of the target were included. This shows a clear performance increase in both the training an testing dataset for both directions. (**A**) shows the training loss. (**B**) shows the validation loss. (**C**) shows the generalisation error calculated by subtracting the training error from the testing error. (**D**) shows the estimated validation error in km. (**E**) shows the estimated validation loss in minutes. (Note that subfigure (**D**) has a truncated \(y\) axis for illustration purposes). Figure 12: Training and validation of the **eastbound direction** for all muted models in comparison to the model with access to the entire network data. In the example of this direction the best validation loss was achieved by the model incorporating the entire network data. (**A**) shows the training loss. (**B**) shows the validation loss. (**C**) shows the generalisation error calculated by subtracting the training error from the testing error. (**D**) shows the estimated validation error in km. (**E**) shows the estimated validation loss in minutes. (Note that subfigure (**D**) has a truncated \(y\) axis for illustration purposes). Figure 13: Training and validation of the **westbound direction** for all muted models in comparison to the model with access to the entire network data.In the example of this direction the best validation loss was achieved by the model incorporating the entire network data. (**A**) shows the training loss. (**B**) shows the validation loss. (**C**) shows the generalisation error calculated by subtracting the training error from the testing error. (**D**) shows the estimated validation error in km. (**E**) shows the estimated validation loss in minutes. (Note that subfigure (**D**) has a truncated \(y\) axis for illustration purposes).