## 6 Conclusions  
Traffic accidents represent a significant threat to human life and are a daily danger. Therefore, the driving dataset was generated with the support of this study. The data collected in this dataset allow us to identify the riskiest points or those with a higher risk level on each stretch of road. The implemented configurations with DL and ML approaches were tested with the driving dataset, which permitted the generation of agile and effective prediction models for traffic accident risk levels. Comparing and evaluating these approaches showed that RBF models were faster at evaluating predictions than DL models. This study concluded that RBFNN models are simpler in configuration and have fewer hyperparameters to consider, contrary to DL network configurations. Furthermore, RBF allows for faster training and comparable efficiency. This fact is advantageous because, when using a dynamic dataset that will continuously be updated with new information, RBF allows us to quickly obtain new predictive models, thereby improving the predictive capabilities with new information. The advantage of working with a dynamic dataset is the ability to adapt this new information to generate more accurate and useful predictions. Hence, the advantage of having an RBF model is that it allows us to find new prediction models agilely, even in real time, due to its processing speed. The DL models showed the best performance results compared with the other models. It is evident that the predictive ability of DL models stands out, and they approach optimal values for a predictive model. However, the most crucial drawback of the DL approach is that the time required to test and train a model is high, and it is estimated that it tends to increase according to the greater amount of information in the dataset used. For example, in this study, a dataset that can be considered small was used, and it has already been confirmed that the times used by the DL models implemented for evaluation and training are high. The MLP model proved to be a good prediction model; its execution times are very good compared to the DL models and only a little slower compared to the RBF models. When comparing the CNN, RF, RBF, and MLP models, the RBF model performed the best, presenting the best execution time with a comparable accuracy performance and prediction efficiency. This work will enable the development of new RBF-oriented models that can accurately predict high-risk events in traffic accidents using a shorter processing time. This advantage will permit other researchers to test these models, which, although relatively simple, generate efficient results. Future work on this topic will involve fully implementing the dynamic dataset and incorporating new data from time to time to evaluate the behavior and execution times when using RBF models to generate predictive models. Moreover, it is possible to identify additional methodologies that could be employed to enhance the accuracy of traffic accident risk level prediction while reducing the time required for training execution. Conceptualization, C.A.-A. ; methodology, C.A.-A. and M.H.-A. ; investigation, C.A.-A. and P.M.; writing--original draft preparation, C.A.-A. ; writing--review and editing, C.A.-A., A.L.V.C. and M.H.-A. ; supervision, A.L.V.C., P.M., and M.H.-A. All authors have read and agreed to the published version of the manuscript. This research was funded by Escuela Politecnica Nacional grant number PIS 22-20 (Development of learning models to predict risk levels of suffering traffic accidents using AI and ML and its application in a system of alerts for mobile devices). Not applicable. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: