## 4 Results and Discussion
### Muting of All Vehicles Except Target
#### 4.1.3. Summary of Muting Effects  
It is expected that the more information about the network is available, the better the performance of the model. This was only partially true. In the experimental investigation, it was shown that the full model with access to all the network data performed better in some scenarios. Figure 8: These figures show the model mutedto the target vehicle itself thus removing all external information. This is compared to network-based model clearly showing that the performance is reduced if the model does not have access to any external 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 9: Per journey data composition of 1000 randomly selected journeys. However, when the model is limited to the headway, this outperforms the network-based model in both directions of travel for trajectory predictions. It should be noted that the difference between headway and line-limited models in the westbound direction is negligible. The generalisation errors for all models are similar. This makes intuitive sense, as, for example, a delay in a vehicle ahead of the target would most likely translate into a delay for the target vehicle itself. However, a disruption of a vehicle running on a different line might or might not affect the target vehicle. This could be different for instances where several lines run on the same road for longer periods of time. Additional information from the entire network was expected to allow better predictions, as delays in one part of the system could propagate throughout the network and thus eventually affect the target vehicle. This was not the case but could be explained by data quality issues, as discussed before. If the interacting lines are severely affected by quality issues, this could, instead of improving the available information, introduce confusing noise, thus making the prediction less accurate. Furthermore, it could be possible that a different choice of interacting lines could produce better predictions. In the presented example, the lines mostly interact at the end of the journey of the target line when considering the area where both travel on the same stretch of road (see Figure 4). When using lines that run in parallel for longer times, this might improve the importance of the interacting lines. Finally, the choice of target line 17 was made because it is the line with the most available data in the network, which means that the data from the line itself will always outnumber the data from the other lines. Figure 9). This results in the fact that the underlying line models of interacting lines will only be updated very infrequently and thus will take much longer to train. Therefore, it can be imagined that if the network-based model was trained for much longer and with more data, it could eventually outperform the headway-limited model. Figure 10: These figures show the model muted to vehicles of the same line regardless of their location in relation to the target vehicle. This is compared to network-based model clearly showing that the performance is reduced if the model does only have access to indiscriminate information of its own line. (**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).