## 5 Correlation Analysis
### Correlation of Travel Time and Disturbance Factors  
After the correlation analysis, the values are shown in Table 5, with Pearson coefficient and significance separated by commas. We grouped similar indicators in adjacent columns for better comparison (\(v_{AV/T}\) with \(v_{50/T}\), \(v_{10/T}\) with \(v_{50/10}\), \(v_{90/T}\) with \(v_{90/50}\), and \(t_{SD}/t_{AV}\) with \(\Delta_{90/10}\)). Since the analysis confirmed that there is too much difference in correlations of behavior of \(v_{10/T}\) and \(v_{90/T}\), or \(v_{50/10}\) and \(v_{90/50}\), there is no point to consider \(t_{SD}/t_{AV}\) and \(\Delta_{90/10}\) in further analysis, i.e., the travel time of early and late vehicles is significantly different, and should be observed independently. Moreover, the values indicating strong correlation are not higher for these indicators if the same predictors are observed. Although \(v_{AV/T}\) shows better values than \(v_{50/T}\), the difference is not significant enough to choose the arithmetic mean instead of the median for travel time discrepancy, because for transit operators, it is much more reliable to know middle-driver performance. For each transverse predictor belonging to signalized intersections (INTR), since they are very similar and prone to intercorrelations, all predictors except \(p\) are eliminated from further analysis, because lost time density shows the best correlation. For unsignalized pedestrian crossings (PEDE), there is not a single significant correlation, so all corresponding transverse predictors are eliminated from further analysis. This is not surprising, since on-field experience showed that pedestrians choose to cross the road when they conflict with private cars, but not when conflicting with trans, speculating pedestrian generosity to yield to highly occupied vehicles or large vehicle size preventing the pedestrians to be confident when crossing the street. This pedestrian reluctance was noticed even at low tram speeds, where tram drivers could easily stop the vehicle before a pedestrian crossing. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Predictor** & \(v_{AV}\) &  
For stops (STOP), all predictors will be excluded from further analysis except the lost time density (\(p\)), showing significant correlation with \(v_{50/10}\) and \(v_{90/T}\) which is better than the other transverse predictors. However, their correlation is negative, meaning roughly that a higher number of stops has less impact on travel time variability, which, although not plausible, is not impossible. Regarding segment length and maximum speed, as it was suggested, it is confirmed by the correlation analysis that the space component in this analysis is redundant since minimum travel time would produce better results, even if modeled poorly. When choosing between the minimum travel time and its reciprocal, since most predictors have minimum travel time in the denominator, the reciprocal following the same logic would naturally be the better choice. Segment frequency, although believed to be significant, showed no correlation. In practice, the minimum transit vehicle interval is one minute for practicality, because stops are the critical points for the capacity, and one minute is considered as time from the moment the vehicle front end entering stop space to the vehicle back end leaving stop space. That one minute per vehicle corresponds to 60 v h\({}^{-1}\) frequency, which was not achieved at any segment. Regarding section versus segment time-share, as expected, there are little correlations, with yellow lanes, white lanes, segregated lanes, intersections, and stops. Only intersections with tram tracks and pedestrian crossings showed significant correlation with some of the indicators. This confirmed that critical car flow share is a much better predictor since it includes traffic volume.