## 1 Introduction
According to the German guidelines for the estimation of capacity and determining the level of service for transportation facilities, given in the Handbuch fur die Bemessung von Strasenverkehrsanlagen (HBS) [5], the average dwell time at an urban rail stop should be calculated from field-obtained information. If this information is not available, an approximate mean stop dwell time of 25 s can be assumed. According to the American guidelines given in the Transportation Research Board's Highway Capacity Manual (HCM) [6], the procedure for determining the stop dwell time considers the number of channels per door for moving passengers, the time of door opening and closing, the number of passengers transiting through the busiest doors, and the time needed for passengers to board and/or alight (depending on the type of flow and floor height). All other factors that are variable in both space and time, i.e., variations in passenger volumes and deviations from the timetable, are considered through an operating margin. This is the extra time added to a line's headway to allow for irregular operation and to ensure that one vehicle does not delay the following one. It is suggested that a range from 10 s to 30 s should be considered for the operating margin. It should be lower the higher the frequencies of the vehicles are. When capacity is not an issue, 25 s or more is recommended. The operating margin can be reduced to 20 s, or even 15 s if necessary, to provide sufficient service to meet the estimated demand. Both guidelines emphasize the need to conduct individualized stop dwell time measurements that consider local conditions. Therefore, to optimize and increase the accuracy of the rail PT timetables, this issue is being researched worldwide in many cities that have established rail PT networks. In urban rail and metro systems, due to the complex task of dispatching trains around a large network and allowing for their long stopping distances, vehicles are guided by a complicated signaling system. No two vehicles can be on the same signal block section (for example, between two stops) at the same time due to the limited capacity of the infrastructure. When building robust timetables, the data used are those that can be measured and made available to traffic control, and they normally only include vehicle positions and timings. For instance, according to [4], the estimation of dwell times at short train stops during peak hours was possible even without passenger data. Accordingto [7], recent studies have aimed to provide a theoretical benchmark for the train traffic rescheduling problem with a simplified description of infrastructure, signaling systems, and rolling stock characteristics. At the same time, few approaches have considered additional parameters such as the number of passengers, mass, performance, and dynamic characteristics of vehicles [7]. These are being recognized as important, influential factors in rail vehicle travel speed investigations and modeling performed to provide stable, feasible, robust, and resilient (sub)urban train and metro timetables [8]. [PERSON] et al. developed a model for use in metro stop dwell time calculation as a function of travel demand flows evaluated by explicitly simulating user behavior on the stop platform when a train arrives and by assuming the starting position of passengers on the platform [9]. [PERSON] et al. proposed future research regarding different sources of uncertainty, especially in terms of the impact of passenger traffic on stop dwell time [10]. According to [3], the uncertainties in stop dwell time present an especially big problem for long tram routes with consecutive stops that are relatively close to one another. The distance between two successive tram stops depends on the route location (close, wide, or outside the city center), and it varies from 200 m to 800 m [11].