## 4 Results and Discussion
### Findings in Context with Previous Results  
Both datasets are of different sizes (westbound 12,478 journeys and eastbound 23,960 journeys). As an approximation, we assume a dataset of 12,500 journeys, while our previous paper used 17,115 journeys with an average error of approx. 120 m [7] and showed that this can be improved by incorporating synthetic data [37]. However, because the interacting lines only represent \(\approx\)5%, of the total data this is equivalent to \(\approx\)625 journeys for each of the interacting lines. Therefore, to make the dataset equivalent to our previous one for all interacting vehicles, this means the dataset has to be increased by a factor of 27 (17,000/625 = 27).  
Due to the processing time currently needed, this would become prohibitive because of the processing time. A dataset of \(\approx\)12,500 requires \(\approx\)30 min per epoch. If we assume a linear scaling of processing time (this is not fully true as the eastbound dataset does not scale linearly but the run time increases marginally slower than if it was scaling linearly), this then indicates a required epoch time for 30 min \(*\) 27 = 13.5 h.  
Superconvergence as used in our previous paper by employing the one-cycle policy speeds up the process by approx. 5 fold. This means that the 50 epochs used previously would be equivalent to approximately 250 epochs without a one-cycle policy [41]. As a result, a total processing time of 140 d can be expected, making such an experiment prohibitive.  
It should be noted that these values are based on the number of data points in the area of interest as defined in Figure 2 and therefore represent only a small part of the traffic system. Another way to look at the problem would be to assess the percentages of data points in the final dataset. These show a different picture as each vehicle can be found in Figure 15.  
Figure 16: Comparison of the eastbound headway muted model trained on a partial as well as a full dataset. (**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 15: Comparison of the westbound headway muted model trained on a partial as well as a full dataset. (**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).  
several samples for different target vehicles. This means that data points related to a target vehicle can be outnumbered by data points from other lines. This means concretely that more data for interacting lines can be present, thus it can be assumed that if more data from the target line were present, this could further improve the model accuracy.