## 1 Introduction
The greater the number of the stops is, the greater the uncertainties of dwell time will be, and the uncertainties of dwell time at each stop will be added up to aggravate the arrival time fluctuation at the next station. A few models that have been created specifically for the calculation of travel speed with regard to tram systems use a great number of assumptions, simplifications, and constraints. In these models, various influential factors regarding tram stop dwell time was contracted and/or neglected, given the availability of data from secondary sources and methods and devices used in field data collection and subsequent data processing and analysis. For instance, models were created with the following assumptions: at any point of time, only one tram can be located at a stop, and dwell times are specific to stop and tram type but always of minimum value [12]; the total tram trip time also includes the dwell and delay time spent waiting on stops and intersections [13]; the dwell time at each terminus is minimal, and a fleet of identical terms only queue at depots [14]; the stop dwell time consists of a deterministic component depending on the passenger flow and a random component depending on meteorological and signaling conditions [15]. Studies on tram stop dwell time presented in [16; 17; 18], performed to identify cause and effect relationships, i.e., how dwell time may be influenced by multiple factors such as boarding and alighting passenger volumes, and vehicle and platform occupancy level, show that these factors certainly have a significant impact on tram dwell time. Additional effects are also found in the stop location and design, vehicle design (number of cars, floor height, i.e., the vertical gap between the platform and the vehicle chassis, door width, and the amount of time required for doors to open and close), payment methods (fare collection systems, i.e., the time that passengers spend by the driver), the time of the day and week (peak, off-peak, inter-peak hours on weekdays and weekends, in accordance with the volume of the passenger and other traffic), and weather circumstances. For instance, a lack of operational symmetry between the directions of a line is noted during the same hours, stops located in different directions of the same line exhibit different dwell time trends [18]. According to [16], stop platform occupancy influence was different for work and weekend days, and the time spent by the driver had the highest effect on tram dwell time. As for vehicle design, according to [19], important differences existed between stop dwell time models for one-and two-car trams because of the typically uneven distribution of passenger movements and loads between cars in a two-car tram. Another observation was the difficulty of running different length trans on the same service at the same time. Unless headways are closely controlled, there is a strong tendency for shorter trams to become heavily loaded and thus run more slowly than longer ones. Depending on the width of the door, the height of the step, and the passenger density inside the vehicle at the start of the alighting process, passenger saturation flows vary between 0.9 passengers/s/m and 2.0 passengers/s/m [20]. The effect of the vertical gap on boarding and alighting time is greater for people over the age of 65, as the average time they need to board or alight is 1.0-1.5 s longer than for younger passenger groups [21]. The effect of stop platform design elements (length and its location) on service reliability must already be considered during infrastructure design [22].