## 1 Introduction  
Crowd management refers to the strategic coordination of pedestrian movement to promote appropriate behavior in public spaces, thereby enhancing the efficiency and effectiveness of pedestrian infrastructure use [1]. It seeks to regulate passenger flows at the platform train interface (PTI) in metro systems to enhance efficiency and reduce delays during boarding and alighting operations. The use of crowd management measuresinfluences the passenger service time (PST)--commonly equated with dwell time--which is defined as the duration that a public transport vehicle remains stopped to transfer passengers [2]. According to those authors, the PST can be obtained through Equation (1), in which \(t_{0}\) is the time of opening and closing of doors, \(t_{a}\) the time for alighting, \(p_{a}\) the number of passengers alighting, \(t_{b}\) the time for boarding, and \(p_{b}\) the number of passengers boarding:  
\[PST=t_{0}+t_{a}\times p_{a}+t_{b}\times p_{b} \tag{1}\]  
Numerous studies have investigated the factors affecting PST, with particular emphasis on elements such as infrastructure layout, crowding levels, and boarding and alighting dynamics. While these contributions have significantly advanced our understanding of PST, the majority of existing research has focused on currently operational stations. This focus inherently constrains the scope of analysis to the existing design and configuration of the PTI, thereby limiting the ability to explore alternative spatial arrangements or operational strategies [2]. In this context, empirical and experimental research methodologies--particularly those involving controlled field studies or simulation-based experiments--offer a valuable opportunity to investigate \"what-if\" scenarios. These approaches enable the assessment of hypothetical PTI configurations and intervention strategies that may not yet exist in practice but hold potential for improving system performance. By combining observations from real stations with systematically varied design parameters, such studies can generate critical insights into how alternative infrastructure or operational modifications might impact PST, affecting the passenger behavior, and accessibility [3; 4; 5]. The infrastructure layout, such as the number and width of doors, significantly affect the rate at which passengers board and alight [4]. For instance, experimental studies show that wider doors facilitate more efficient flows and reduce boarding time. In public transport systems, multiple-door configurations generally decrease PST by enabling simultaneous movement of passengers [3]. With respect to door width and step height, experiments by [PERSON] et al. [4] found that narrower doors and taller steps substantially increase PST. These physical constraints disproportionately affect elderly and mobility-impaired passengers, adding seconds per boarding event. With respect to crowding, PST increases linearly with the number of passengers boarding and alighting, although this relationship can be nonlinear when crowding becomes extreme [6]. Empirical studies also show that group behavior, urgency, and lane formation influence flow rates and waiting times [7]. In addition, congested platforms extend boarding/alighting time, especially when pedestrian traffic is not managed. As noted by [PERSON] et al. [8; 9], crowding increases boarding times by up to 14%, and the presence of a congested aisle can increase alighting times up 68%. Internal density affects how easily passengers can maneuver toward exits. High-density conditions result in greater friction and slower movements, especially for alighting passengers. These studies are in concordance with [PERSON] [10] and [PERSON] [11], who also highlight that internal congestion is a crucial factor in PST variability. In relation to the dynamic of passengers, [PERSON] et al. [12; 13] showed that certain obstacles--strategically placed--can reduce turbulence and encourage orderly flow, effectively reducing PST. Similarly, [PERSON] et al.