## 7 Conclusions  
Travel time variability is important to study because of its impact on timetables and transit service reliability. Establishing the relationships between various disturbances in the network and travel time is key to understanding how the service would perform, preparing the operator for worst-case scenarios. Therefore, it is imperative to understand how travel time is distributed, what are its best indicators, what data aggregations should be considered, and which disturbance factors influence travel time the most. Travel time discrepancy, on the other hand, is a measure of network performance, since it shows the gap between real and ideal conditions, informing the operator and the local community of the current state of transit service.  
This study of travel time variability conducted on the tram network in the City of Zagreb in the afternoon commuter period with a simple data aggregation showed normal and right-skewed distributions, confirmed the possibility of defining the indicators based on minimum travel time for both discrepancy and variability, and established predictors for travel time discrepancy and variability based on disturbance factors, but not based on transport supply irregularity.  
The applicability of this research is most prominent for surface public transport in mixed traffic conditions like the City of Zagreb, where transit is not segregated completely from other traffic, where signalized intersections do not provide priority entirely, and where there are unsignalized pedestrian crossings obstructing normal operations. This research can also cover bus and trolleybus modes as well; however, minimum travel time should be researched in more detail due to even more variations in vehicle and driver performance in bus and trolleybus traffic.  
The next natural step derived from this research would be to construct a travel time prediction model built upon the established correlations. This would produce three data-based models: for discrepancy, and for variability to represent both early and late vehicles. For bus and tram operators, this model serves to predict travel time in addition to the existing models and gets insight into travel time on newly established corridors before providing the timetables. This would ensure better service reliability for public transport and better satisfaction among passengers, contributing to the modal shift of city trips onto more sustainable transport modes.  
Conceptualization, D.S. and M.S. ([PERSON]); data curation, D.S. and M.C.; formal analysis, D.S.; investigation, D.S. and M.S. ([PERSON]); methodology, D.S. and M.S. ([PERSON]); project administration, D.S.; resources, M.C.; software, M.S. ([PERSON]); supervision, M.S. ([PERSON]); validation, M.S. ([PERSON]); visualization, D.S.; writing--original draft, D.S.; writing--review and editing, M.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed at the corresponding author.  
We wish to thank the students of Urban Traffic and Transport at undergraduate and graduate level at the Faculty of Transport and Traffic Sciences for their dedication in data collection. We also thank Zagreb Electric Tram for their cooperation and support.  
The authors declare no conflicts of interest.