## 3 Research Methodology
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Denotation** & **Title** & **Application** & **Unit** \\ \hline \(T\) & Minimum travel time & Independent & min \\ \(t_{MI}\) & 0 th percentile & Discrepancy & min \\ \(t_{10}\) & 10 th percentile & Discrepancy & min \\ \(t_{50}\) & 50 th percentile (median) & Discrepancy & min \\ \(t_{AV}\) & Arithmetic mean & Discrepancy & min \\ \(t_{90}\) & 90 th percentile & Discrepancy & min \\ \(t_{MA}\) & 100 th percentile & Discrepancy & min \\ \(t_{90}-t_{10}\) & 100 th percentile minus and 90 th percentile & Variability & min \\ \(t_{SD}\) & Standard deviation & Variability & min \\ \(\frac{t_{MI}}{T}-1=v_{MI/T}\) & 0\% versus minimum travel time percentile difference & Discrepancy & \% \\ \(\frac{t_{90}}{T}-1=v_{10/T}\) & 10\% versus minimum travel time percentile difference & Discrepancy & \% \\ \(\frac{t_{90}}{T_{10}}-1=v_{50/T}\) & Median versus minimum travel time percentile difference & Discrepancy & \% \\ \(\frac{t_{90}}{T_{10}}-1=v_{50/10}\) & Median versus 10\% percentile difference & Variability & \% \\ \(\frac{t_{90}}{T}-1=v_{90/T}\) & 90\% versus minimum travel time percentile difference & Discrepancy & \% \\ \(\frac{t_{90}}{T_{90}}-1=v_{90/50}\) & 90\% versus median percentile difference & Variability & \% \\ \(\frac{t_{90}}{t_{90}}-1=v_{MA/T}\) & 100\% versus minimum travel time percentile difference & Discrepancy & \% \\ \(\frac{t_{90}-t_{10}}{t_{90}}=\Delta_{90/10}\) & 100 th percentile minus and 90 th percentile versus the median & Variability & \% \\ \(1-\frac{t_{90}}{t_{90}}\) & 10\% versus the median percentile difference & Variability & \% \\ \(\frac{t_{90}}{T}-1=v_{AV/T}\) & Arithmetic mean versus minimal travel time percentile difference & Discrepancy & \% \\ \(\frac{t_{SD}}{T_{AV}}\) & Standard deviation versus the arithmetic mean—coefficient of variation & Variability & \% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Candidates for indicators representing travel time discrepancy and variability. The data were collected for every segment covered by Line 4, which makes 21% of the tram network (Figure 3). The line was chosen for this research because it passes through the most frequent corridors by tram lines, and the influence of other traffic is the highest, which will make the data more diverse and therefore better suited for the analysis. Also, the line has the highest number of yellow lanes (lanes segregated for public transport but prone to contravention) of 64%, which creates the best potential for travel time variability. With this stated, among the 15 daily tram lines, Line 4 is the best representative of the entire tram network, as it has the best coverage for potential disturbance factors in daily operations. The data were collected in the afternoon commuter period for six days from 15:00 to 17:00, since the number of vehicles in operation was constant for the mentioned two hours. The line was split into 30 constant-frequency segments (Figure 4), where lines sharing the same corridor do not change. The shortest segment is 0.1 km in length, and the longest one is 1.9 km in length. Segments 01 to 15 are on the route to Dubec terminal, and segments 15 to 30 to the Savski most terminal. At each segment border and at terminals, students registered the times and vehicle types. Additionally, the students collected data about red and green traffic light times, and critical vehicle flows. Since all the segments on the line were included, and all vehicles were registered (including the vehicles of every other line sharing the segments with Line 4), for the observed afternoon commuter period, the sample for Line 4 represents the population. Figure 3: Zagreb tram network (in red), and Line 4 (in blue) in the urban area of the city. Each terminal is numbered with its corresponding lines.