## 8 Conclusions  
Station overcrowding indices are introduced to obtain more reliable results for risk management in real-time, leading to systematic, manageable, and identified hazardous events. Overcrowding risk prediction can be used to help analyse risk levels and ensure that a high level of safety is achieved in stations, where passenger density is one of the most critical indicators for the safety and quality of service. It can be concluded that the suggested system has the potential to provide very realistic vulnerability assessments of overcrowding in stations. Certain features, such as the relationship between risk indices, are highlighted. Furthermore, the proposed method can help create novel ways in ISFMR modelling for stations and support risk management sciences based on risk indices. Hence, the evolution of AI technology and big data makes this process more intuitive and timely for decision makers working in railway systems, and at stations in particular, contributing to more efficient outcomes than conventional approaches. The data extracted from Beijing's Guomao metro station with the two parameters (retention rate index and transfer efficiency index) have been analysed and trained using supervised machine learning, and three optimizable models conducted: regression trees, the Gaussian process regression model (GPR), support vector machines (SVM), and ensembles of regression trees. The prediction results indicate that the Gaussian process regression model (GPR) model shows high accuracy in predicting the risk level performance where the RMSE = 0.0064, and the coefficient of determination (R\({}^{2}\)) = 1. It seems that the hyperparameter optimization model's performance shows a high level of fit to the data. Generally, it is thought that this is due to the acceptable performance  
Figure 17: Comparison of the proposed model between predicted risk level and the observed. and a high accuracy modelling for estimating the level of risk. The models enhance the proposed framework for predicting the risks in real-time and supporting decision makers with low human error and learning from the history of the flow in the stations. A dynamic risk management model for railway stations using ANFIS has been developed, which can deal with active risk data indices in real-time. The ANFIS is one of the most successful techniques in terms of its ability to predict risk in railway stations. The selected model appears robust for predicting risks and aiding decision making for dynamic risk management in relation to the danger of overcrowding in railway stations and is a step toward smart future railway stations. The model has the flexibility to deal with a significant number of inputs for future research, which is not an easy task. The ANFIS appears to be more reasonable and appropriate because of its smoothness in calculating the risk levels. In addition, risk can be evaluated effectively from the source of the knowledge base built by modifying data from various sources and indices. Furthermore, the model shows faster learning and higher prediction accuracy. Risk indices are essential to reflect the actual condition of the station and indicators at the early stage of risk factors such as overcrowding. The dynamic risk management model can define risk level and aid decision makers by providing convenient and reliable results based on real-time data. In practice, the ANFIS prediction model provides a decision-support system for managing risk that can be captured through indicators or indices, which creates the rules depending on the inputs. The necessary inputs can be gathered from devices (such as sensor-based programming or CCTV), and the estimate can be calculated with the ANFIS system, generated from servers at the railway stations. Predictions of risk based on some indices can then be presented to decision makers through a web-based program. It is expected that if more data and training are passed through the ANFIS model as input, more accurate results can be obtained.