## 5 Discussion
### Comparison with Existing Empirical Studies and Best Practices
A pedestrian simulation method provides a consistent framework for simultaneously evaluating metrics such as passenger density, waiting times, and counterflows;
* Flexibility of the simulation tool: By using modeling software that integrates both the train dimension and the pedestrian dimension, a comprehensive analysis of platform behavior was achieved--an aspect that many previous studies address separately (either focusing solely on pedestrian flow or exclusively on train frequencies);
* Reference to recognized standards: Basing our work on Fruin's level of service (LOS) scale and density metrics validated by the literature ensures that the parameters used are compatible with internationally accepted evaluation methods, allowing us to compare our findings with those of other studies;
* Representation of real-world scenarios: By considering both symmetric and asymmetric flows, a broader spectrum of potential operational conditions was considered, thus facilitating comparisons with empirical research that examines maximum occupancy situations, peak hours, or unidirectional demand. Thus, the methodology used is well-suited to the requirements of comparative analysis and, furthermore, allows us to identify points of alignment with studies that yield similar outcomes, while still recognizing discrepancies that arise when demand levels, cultural conditions, or growth assumptions differ from those in our model. However, with respect to the limitations of this study, the model is a simplified abstraction of reality, aiming to compare the benefits of different platform configurations under various conditions. There are potential limitations and biases worth discussing to ensure the applicability of the results to broader contexts:  
* Heterogeneity of passengers: The model treats users in a relatively homogeneous manner, assuming they all behave similarly in terms of walking speed, reactions to congestion, or boarding/alighting methods. In reality, however, there is great variability (people with reduced mobility, passengers with large luggage, children, groups, etc.). Failing to account for this diversity of passenger profiles can introduce bias by overestimating the station's real efficiency or, conversely, by overlooking specific bottlenecks (for example, slower queues on stairways or elevators). Nonetheless, since it is a comparative analysis, these biases and limitations would appear similarly in all scenarios;
* Fixed-demand assumptions: The study begins with predefined flow scenarios (symmetric/asymmetric, high/low). While these scenarios cover a reasonable range of conditions, real station demand can fluctuate due to seasonal factors, large-scale events, extended rush hours, failures in other transportation modes, etc. Consequently, the limitation of using fixed-demand patterns makes it difficult to extrapolate the findings to unforeseen or highly volatile situations. However, these constraints would apply equally to all scenarios;
* Lack of exhaustive empirical validation: Although the model is calibrated according to parameters from the literature (for example, Fruin's guidelines, maximum densities, and level of service), ideally one would have broad empirical data from operational stations to assess how well the simulations match reality (e.g., comparing observed vs. simulated densities and travel times). Without such validation, the results remain an approximation that may differ in real-life situations, especially in infrastructures with unique architectural features or variations in users' physical characteristics (societies with higher obesity rates or older populations may exhibit different behaviors and interactions with the infrastructure);Specific geographic and cultural context: Station usage and circulation conditions may vary depending on local culture and pedestrian norms (e.g., queuing customs, attitudes toward congestion, accessibility regulations).