## 6 Discussion
Subsequently, \(v_{90/T}\) can be mathematically adjusted to the median to represent variability instead of discrepancy. The best classification of predictors is, according to this study, into transverse and longitudinal, because this classification enables the following: better understanding from a traffic perspective, simple representations and comparisons because the predictors of the same category act similarly, and prevention of the cumulative effect and collinearities. However, the limitation of resources in this study resulted in many important disturbance factors used in previous research such as weather conditions, land use, or periods during the day, to be ignored in this research. Besides the categorization into transverse and longitudinal groups, the richness of traffic context in this research is emphasized by defining the minimum travel time used as a base to establish correlations, making predictors based on space imprecise. And although the introduction of minimum travel time produced significant correlations, the risk in modeling minimum travel time includes driver and vehicle performance, especially for diverging vehicle characteristics and driver behavior. Minimum travel time in this study enabled observing travel time separately as discrepancy and variability, currently scarce in the literature. Due to the limitations of data collection, stop times could not be observed independently of driving times between the stops. Stops, however, were included in limited forms in both the predictors and minimum travel time, and there were no significant correlations between them and travel time. As was shown in the literature, stops must be observed independently, because factors influencing stop dwell time and riding time significantly differ. To reason observing travel time without separation in this study, we relied on stopping time being relatively smaller than riding time (usually 15% of travel time belongs to stopping time). There were two types of predictors represented in this research--based on disturbance factors, and transit supply irregularity. The latter showed only correlations for \(v_{90/50}\), and therefore, we reject the hypothesis H2, because both discrepancy and variability did not correlate. On the other hand, the disturbance factor hypothesis H1 is confirmed because there were significant correlations to six predictors: lost time (signalized intersections, stops), minimum travel time reciprocal, section versus segment time-share (tram intersections, unsignalized pedestrian crossings) and car critical flow share. We suspect that section versus segment time-shares do not have a sound basis for correlations in traffic environment, and it remains to be seen whether they would show significant in travel time modeling. This research was conducted on a single tram line in the City of Zagreb. Although the line makes 21% of the total tram network length, and we elaborated why the line is by far the most potent for travel time discrepancy and variability in the network, this research could have problems with generalizability, since the other parts of the network, despite having less emphasized disturbance factors, could show problems not detectable on Line 4 route. An example of this is Ilica Street in the city center. Although this is a pedestrian-tram corridor with shops, drivers tend to slow down trams anyway because pedestrians walking alongside tram tracks can unpredictably cross the tracks, so the drivers always anticipate the reckless pedestrians, with minimum braking distance possible. This was not covered in minimum travel time modeling. Therefore, the next step in strengthening the results of this research is, for the remainder of the tram network, to outline the potential segments where different traffic conditions could be expected, and to conduct the analysis on them.