## 4 Determining Travel Time Indicators and Predictors
### Determining Travel Time Predictors
However, the reciprocal is proposed because, contrary to the minimum travel time, the reciprocal is not a linear function. Segment length and maximum speed are also included. Timetable frequency was already used in determining transverse factors related to stop cycle time. It varies among the segments such that more lines sharing the same segment generate higher frequency. In the collected data, it has a range from 11 to 44 vehicles per \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Formula** & **Further Explanation** & **Unit** \\ \hline Segment length & \(L\) & \(L\): segment length & km \\ Segment minimum travel time & \(T\) & \(T\): minimum travel time & min \\ Segment minimum travel time reciprocal & \(T^{-1}\) & Unit: percent per minute & \% min\({}^{-1}\) \\ Segment maximum speed & \(V=L\)T\({}^{-1}\) & Unit: meters per second & m s\({}^{-1}\) \\ Segment timetable frequency & \(f\) & Unit: vehicles per minute & v min\({}^{-1}\) \\ Section versus segment time-share & \(t\) & For each of the seven section types & \% \\  & & \(j\): number of sections at the observed segment & \\ Critical car flow share & \(q=\frac{1}{T}\sum\limits_{j=1}^{J}q_{j}t_{j}\) & \(q_{j}\): car flow criticality (0 or 1) at section \(j\) & \% \\  & & \(t_{j}\): minimum travel time at section \(j\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Proposed longitudinal predictors and their calculation for segments. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Formula** & **Further Explanation** & **Unit** \\ \hline Disturbance factor density & \(g_{I}=\frac{I}{T}\) & \(I\): number of disturbance factors on the & \\  & & observed segment & min\({}^{-1}\) \\ Red light density & \(p_{R}=\frac{1}{T}\sum\limits_{i=1}^{I}R_{i}\) & \(R_{i}\): red light duration at disturbance factor \(i\) & \% \\ Density of double red versus & \(g_{2R/C}=\frac{2}{T}\sum\limits_{i=1}^{I}\frac{R_{i}}{C_{i}}\) & \(C_{i}\): cycle duration at disturbance factor \(i\) & min\({}^{-1}\) \\ Density of red versus green & \(g_{R/G}=\frac{1}{T}\sum\limits_{i=1}^{I}\frac{R_{i}}{C_{i}-R_{i}}\) & \(C_{i}-R_{i}\): green light duration at disturbance factor \(i\) & min\({}^{-1}\) \\ Lost time density & \(p=\frac{1}{2T}\sum\limits_{i=1}^{I}\frac{R_{i}^{2}}{C_{i}}\) & \(t_{i}\): average lost time at disturbance factor \(i\) & \% \\  & & \(\frac{t_{i}}{R_{i}}=\frac{R_{i}}{\Sigma_{i}}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Proposed transverse predictors and their calculation for segments. hour. We assume that more frequency means more vehicle bunching, which has an impact on travel time, especially for higher values. Since timetables are not adjusted to support evenly distributed vehicles even from source terminals, we assume that timetable frequency is a good enough indicator in this research, although in practice, vehicles become more unevenly distributed as they get closer to the destination terminal. Section versus segment time-share is calculated for each of the seven section types. The basic assumption is that at least yellow or white lanes could have some impact on travel time, although the traffic on those lanes is generating problems, not the lanes themselves. In the City of Zagreb, private car flow is especially dominant in the afternoon commuter periods, because although it is smaller by peak volume, it lasts longer than the morning commuter period. The flow can often congest the lanes shared by transit vehicles, whether legally in white lanes, or by yellow lane contravention. The congestion is often aided by red lights at intersections, vehicles parking alongside tram lanes, or pedestrians at intersection crossings preventing private cars from clearing the lane.