## 4 Determining Travel Time Indicators and Predictors
### Determining Travel Time Predictors  
Based on experience in public transport and the previous research, we proposed several predictors for travel time discrepancy and variability. Since this is fully traffic-related research, the most interesting classification for us is by space distribution, since we assume that each candidate of the same group will be represented mathematically in a similar fashion:  
* Transverse disturbance factors occur in a single point on the network, acting as an obstacle; because they are physically separated, we assume that there would be no cumulative effect and intercorrelations; usual examples are signalized intersections, unsignalized pedestrian crossings and stops;
* Longitudinal disturbance factors occur throughout the entire section of a segment or the entire segment; since longitudinal disturbance factors can overlap on sections or segments, there is a potential for cumulative effect and intercorrelations between the predictors; usual examples are transit vehicle flows, other vehicle flows, segment length, minimum travel time, or maximum travel speed. Since this study considers minimum travel time as the starting point, the predictors will be represented relative to minimum travel time as a much more precise measuring unit instead of units based on space. Also, this will ensure every predictor (even the ones that are counted) to be defined as a continuous variable, usually named as densities. Besides the interactions among the longitudinal disturbance factors, a cumulative effect can also occur based on interactions between transverse and longitudinal disturbance factors. In this manner, we experimented with multiples of frequency and transverse predictors as well as exponential and logarithmic variables, but there were no significant correlations, concluding that the cumulative effects can be neglected based on the data. The transverse predictors we propose are presented in Table 3, such that every transverse disturbance factor (signalized intersection--INTR, unsignalized pedestrian crossing--PEDE, stop--STOP) can produce any of the predictors we defined. Values for each predictor were calculated and presented separately. Figure 8: Chosen travel time indicators showing variability for further analysis for each segment. Red light duration, although not intuitive, can be determined for other disturbance factors besides the signalized intersections as well. At unsignalized pedestrian crossings, red light duration is represented by the time the crossing is occupied by pedestrians. At stops, the red light begins when the front end of the vehicle enters the stop area, and it terminates when the back end of the vehicle leaves the stop area. Double red versus the cycle was selected to be able to compare the values to the other proposed predictors, since one per minute in this fashion is a critical point where red light makes half of the cycle. The cycle at signalized intersections is naturally the period in which all phases exchange in order. At unsignalized pedestrian crossings, an arbitrary time of 10 min was selected for a precise enough measurement, so combining the total time the crossing was occupied by pedestrians produced the cycle. For the stops, the cycle is the multiple of the vehicle interval and the number of platforms at the stop. For lost time density, a simple case without acceleration and deceleration was observed, and in it, transit vehicles arrive uniformly at a disturbance factor. In these conditions, the number of vehicles that arrived at the green light is \((C_{j}-R_{i})/C_{i}=1-R_{i}/C_{i}\), and each of those vehicles lost zero seconds at the intersection. On the other hand, the remaining \(R_{i}/C_{i}\) vehicles arrived at the red light, with the average waiting time being half of the red light length. Proposed longitudinal predictors are shown in Table 4. Although minimum travel time is the basic parameter of the analysis, it is proposed as a predictor because there is a possibility for it to generate correlations.