## 4 Assessment of Experimental Results Using Sensor Technologies and Models  
To assess the results from laboratory experiments, different methods can be used (see Table 2). Sensor technologies like PeTrack [59] enabled precise tracking of pedestrian trajectories, calibrated with methods derived from spatial models. The study conducted by [PERSON] and [PERSON] [56] quantified the space occupied by a wheelchair user in a preferential metro platform waiting area under two density scenarios: medium (1.5 passengers/m\({}^{2}\)) and high (4.0 passengers/m\({}^{2}\)). Using full-scale laboratory experiments and PeTrack version 1.0 motion-tracking software, the researchers measured spatial variables such as area occupied, average distances between users, and lateral/frontal clearances. The results indicated that at medium density, wheelchair users occupied approximately 2.12 m\({}^{2}\), whereas at high density, this decreased to 1.42 m\({}^{2}\), a 33% reduction. This finding highlights how increasing crowd density compresses personal space, even for vulnerable users. This disparity is particularly relevant because traditional LOS metrics do not account for such spatial outliers. Furthermore, the study found statistically significant differences in the spatial distribution (lateral, frontal, and rear distances) as density increased. These differences suggest that wheelchair users' space needs cannot be parameterized by fixed geometric shapes like ellipses, supporting the argument by [PERSON] et al. [57] that personal space is dynamic and asymmetric. When compared with passengers without disabilities at the same high-density level, wheelchair users occupied 61% more space on average (1.42 m\({}^{2}\) vs. 0.86 m\({}^{2}\)). This difference is explained by surrounding passengers increasing their distance from wheelchair users, a behavior previously observed by [PERSON] et al. [60]. The study concludes that current station designs and LOS frameworks inadequately reflect the needs of passengers with disabilities and proposes a methodological framework for measuring occupied space that better supports inclusive infrastructure design. Figure 1: Multidimensional LOS proposed as a framework at the PTI. With respect to models, several studies have introduced simulations to quantify and predict crowding under varying configurations. [PERSON]'s LOS model laid the early groundwork for space and density estimation, but more recent work by [PERSON] [61], [PERSON] [63], and [PERSON] et al. [19] emphasizes dynamic flow behavior and psychological comfort, especially in high-density environments. Simulation platforms like Legion and VISSIM have been deployed in similar contexts to test station layouts and PTI designs [64; 65], with verified accuracy in representing pedestrian movement [66; 67]. More recently, study [62] proposes the use of factorial design and pedestrian simulation to optimize PTI configurations based on passenger LOS. The researchers modeled the Valparaiso Metro PTI using simulation software and assessed combinations of five factors: number of doors, boarding/alighting ratio, distance between doors, passenger density, and door width. Key metrics included pedestrian travel time, the number of interactions, and average speed, which together represent a comprehensive LOS indicator. The results demonstrate that door width and passenger density were the most significant factors affecting LOS. Wider doors significantly reduced interaction and travel time, improving average speed and user comfort. Higher passenger density, on the other hand, led to increased interaction and congestion, particularly near door zones. The interaction between boarding/alighting ratio and density also revealed nonlinear effects on the LOS, indicating that optimal design solutions must be context specific. The number and distribution of doors affected the flow distribution but had lower statistical significance than door size and density.