## 1 Introduction
The most influential disturbance factor commonly present in commuter periods is crowding in vehicles or at stops, where high average occupancy levels also increase the probability of vehicles circulating full, and therefore, not being able to pick up passengers waiting at stops and stations, which increases waiting time and travel time variability [12]. To tackle disturbance factors and reduce travel time discrepancy and variability, intelligent public transport systems and big data systems are most promising [13]. Technologies that enable better operations are automatic vehicle location, automated passenger counting,  
Figure 1: An overview of disturbance factors classified according to manageability [1]. user information, geographic information systems and decision-support systems [14]. Two most popular implementations are:  
* Vehicle priority--to speed up vehicles by giving them a privileged status in urban traffic either by legislation, by segregation, or by adjusting green light at intersections;
* Vehicle holding--to slow down vehicles such that sacrificing speed causes more evenly distributed vehicles in time; this can be done by punctuality considering the timetables suitable for lower frequencies, or by regularity considering real interval corrections better suited for higher frequencies [1]. To improve the reliability of urban surface transit, our aim in this research was to establish correlations of both travel time discrepancy and variability with different traffic-based disturbance factors. For this, first we modeled the minimum travel time based on ideal conditions. Then, on this minimum travel time, we established possible indicators of travel time discrepancy and variability. Then, we selected various variables as predictors from real-time traffic data. Finally, we established correlation matrices between the predictors and the indicators using Pearson correlation coefficients and significance to conclude which indicators and predictors show the best correlation. There were two types of predictors: based on disturbance factors and based on transport supply irregularity. The significance of this research is in determining both travel time discrepancy and variability, since the research usually considers variability only. And since this type of research is conducted dominantly in bus transit, this research in tram transit provides conclusions on correlations between disturbance factors and travel time in mixed traffic environments otherwise unusual for transit on tracks. This research checks two hypotheses:  
**Hypothesis H1. **_It is possible to correlate travel time discrepancy and variability with disturbance factors._  
**Hypothesis H2. **_It is possible to correlate travel time discrepancy and variability with transport supply irregularity._  
This paper is composed of six main sections. Section 1 is the introduction stating research context, aim, purpose, significance and hypotheses. In Section 2, we present previous research about the subject and outline the novelty of our research. In Section 3, we present research methodology, encompassing the details on data collection and analysis. In Section 4, we outline travel time indicators and predictors, followed by the correlation analysis in Section 5. Section 6 discusses the research results, and Section 7 is the conclusion.