## 7 Results and Analysis  
Data were collected during the peak hour 07:30-08:30, with a super peak occurring at 07:45-08:15, and were used to calculate the retention rate. Moreover, to obtain the stranded ratio, we have considered the loading factor (1.05-1.02) for our calculations. Next, the index for the transfer efficiency from channels was calculated; this included the factors of walking velocity (1.5 m/s) and passing time (107 s). The total transfer time, including the time required to cover the distance to the platform, was 152 s. The data of the transfer efficiency show that after 08:00, the congestion remained at a steady rate, and passenger movement was smooth. The congestion risk level fluctuated between very likely and unlikely, as shown in Figures 6 and 7. Figure 6: Correlations between the inputs and the risk level. The Neuro-Fuzzy Designer has been used as a tool for the model, design, training, and testing in Sugeno-type fuzzy inference systems. The precalculated indices and risk matrices have been utilised for training the system. We selected a partitioning method where grid partitioning is the default scheme of the FIS structure generation with ANFIS, as shown in Figure 10a. From the chosen data, datasets were applied to train the model and the residue was used for assessment and validation of the  
Figure 8: Multi-comparison performances scatter plots of the predicted and actual values for different types of regression learner optimizable models: (**a**) prediction (Optimizable SVM), (**b**) prediction (Optimizable Tree), (**c**) prediction (Optimizable Gaussian Process Regression Model (GPR)) and (**d**) prediction (Optimizable Ensemble). Figure 9: Minimum mean squared error (MSE) for the various regression learner optimizable models:  
(**a**) Optimizable SVM, (**b**) Optimizable Tree, (**c**) Optimizable GPR, (**d**) Optimizable Ensemble. system. Moreover, for rule generation, subtractive clustering was used, and the extent of influence, squash factor, acceptance ratio, and rejection ratio were set at 0.5, 1.25, 0.5, and 0.15, respectively (see Figure 10b). The MFs can vary in number and are assigned to each input in the ANFIS model. We have also estimated the risk indices in two segments. Figure 11. Shows the latent class estimation results assuming homogeneity between the indices and the total risk. This indicates the importance of the platform design and punctuality in train schedules and loads to meet the waiting passenger volume. A low risk level indicates a safe platform and smooth passenger flow; however, other platforms or locations may be overcrowded at the same time. Figure 11: Heat map showing the correlation between the inputs and the risk levels. Figure 10: (**a**) ANFIS model architecture for overcrowding risk level prediction where the branches show the characteristics of the ANFIS model structure beside the feed-forward network; (**b**) ANFIS network after applying subtractive clustering. The trained FIS includes 16 rules (clusters), as presented in Figure 12. Each input has 16 Gaussian curve built-in MFs. During training in ANFIS, sets of processed data were used to perform 150 cycles of learning. To consider the relationship between the input variables (risk indices) and the output variable (risk level), several fuzzy sets were assigned to the output data using the Sugeno-type FIS method, which is illustrated in Figure 13.  
The ANFIS models are evaluated based on their performance in training and testing sets. Various experiments were conducted with different MFs type inputs, the sizes of 80% of the data used to train the ANFIS and the rest as used to validate the accuracy of the model (test dataset). A random subset used for testing and RMSE values has been used to present the best membership functions and to better reflect the model performance differences [111]. For a comparison of RMSE values for the training and testing process, the experiments cover different MFs, a linear MF types output.