###### Abstract  
A complex AI system must be worked offline because the training and execution phases are processed separately. This process often requires different computer resources due to the high model requirements. A limitation of this approach is the convoluted training process that needs to be repeated to obtain models with new data continuously incorporated into the knowledge base. Although the environment may be not static, it is crucial to dynamically train models by integrating new information during execution. In this article, artificial neural networks (ANNs) are developed to predict risk levels in traffic accidents with relatively simpler configurations than a deep learning (DL) model, which is more computationally intensive. The objective is to demonstrate that efficient, fast, and comparable results can be obtained using simple architectures such as that offered by the Radial Basis Function neural network (RBFNN). This work led to the generation of a driving dataset, which was subsequently validated for testing ANN models. The driving dataset simulated the dynamic approach by adding new data to the training on-the-fly, given the constant changes in the drivers' data, vehicle information, environmental conditions, and traffic accidents. This study compares the processing time and performance of a Convolutional Neural Network (CNN), Random Forest (RF), Radial Basis Function (RBF), and Multilayer Perceptron (MLP), using evaluation metrics of accuracy, Specificity, and Sensitivity-recall to recommend an appropriate, simple, and fast ANN architecture that can be implemented in a secure alert traffic system that uses encrypted data. deep learning; radial basis function; prediction of traffic accidents; dynamic dataset +
Footnote †: journal: Medical  
These facts motivated the search for practical solutions to prevent more lives from being lost due to traffic accidents. An interesting proposal, mentioned by [PERSON] et al. [3], is to use the large flow of traffic data that can be obtained and, through the use of DL and ANNs, develop predictive models to reduce the risk levels of traffic accidents, which can be implemented in effective risk warning systems for drivers. However, it is important to note that obtaining good prediction accuracy of the risk of traffic accidents is complicated because it is related to several factors [4], like weather and road conditions, which affect the effectiveness. Another reason is that various conditions differ from one region to another. [PERSON] and [PERSON] [5] mentioned that predicting traffic accident risk remains challenging due to many factors contributing to accidents, including the number of vehicles on the road and external conditions like weather, road conditions, ambient lighting, and time of day. They also indicated that recent studies have attempted to combine various factors using complex models to make better and more precise predictions. ML methods have been extensively utilized in traffic prediction problems, allowing for the prediction of multiple crash injuries using data that include different causes and factors from events on roads and streets [6]. Several studies [7; 8] have explored and analyzed various types of ANNs and concluded that the Multilayer Perceptron neural network (MLPNN) is the most commonly used ANN for predicting road accidents. They also found that, in some cases, the RBFNN has better predictive performance than the MLPNN, but this difference could be due to several factors. However, [PERSON] et al. [9] state that predicting traffic accident risk requires a lot of data. Therefore, many researchers have turned to DL to develop models for accident risk analysis. Modern DL networks usually consist of tens or hundreds of successive layers to discover complex structures in high-dimensional data and to extract hierarchical representations in feature learning [10].