## 7 Results and Analysis
The surface plot shows that the three-dimensional (3D) surface poses a challenge to the evaluation of the two indices, which have a significant impact on the expected overcrowding prediction (see Figure 16). In general, it can be inferred from the 3D diagram that the risk level increases with the increase in the index probability, which is a logical conclusion. For other risk indices, the same procedure can be followed. This model is implemented using the selected simple-data sample obtained from the literature to reach conclusions about the effectiveness of the method. The results show the surface and counters of unsafe index levels and the impact of the index of the input obtained in risk level results. Regarding the selection of the best MF, different scenarios are considered, and the scenario with the smallest error is accepted. Regarding the performance of the designed FIS, different RMSEs are observed that indicate a perfect fit to the data. A 3D surface of all possible values of inputs (indices) of the ANFIS model and the output corresponding to these values has been presented. Variations between the results for different MFs are evident from the 3D surface plots. The response of the 3D surface of risk level versus the indices shows deficient minimal errors depending on the MFs selected. It can be inferred from these figures that the high flow from both indices results in a high overcrowding risk level. The increase in the number of passengers on the platform or in other areas of the station will result in an increasing density, which is predictable. The ability of the prediction model is improved by increasing the size of the training and  
Figure 16: Example of the 3D surface plots of the rule base system adapted for the data for different mf types; (**a**) dsigmf, (**b**) gbellmf. Figure 15: Training data and FIS output with two inputs and one output. testing datasets. We can provide many 3D models for training with different MFs to create greater clarification and a more general comprehension of risk factors which need to be managed and of risk indicators in railway stations. The results of the observed data are compared with the ANFIS, as shown in Figure 17, which displays the assessment results from the ANFIS. The above analyses elucidate that the proposed model of ANFIS can predict the risk and represent their statistical features with reasonable accuracy. In the proposed framework, the dynamic risk can be managed in real-time, which supports the decision making and reflects the speed of the reaction to manage overcrowding risks. Such a framework can use input parameters as the transfer efficiency and retention rate of the platform, in order to predict the flow safety performance. It should also be mentioned that the proposed model can provide great estimations of safety performance, especially for estimating the number of staff needed and for the advance measures of the platforms and machines at the project planning stage of the railway stations. This has to raise the importance of prediction more than any time in the past, as the crowding has led to the spread of COVID-19 and thus affected the safety, security and health concepts and strategies.