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.

The problems with current driving datasets are their exclusivity to autonomous driving applications and their limited diversity in terms of sources of information and number of attributes. Thus, this paper presents a novel driving dataset that contains information from several heterogeneous sources and targets road traffic safety applications. We used an acquisition module based on software and hardware to collect information from a vehicle scanner and a health monitor. This module also consumes information from a weather web service and databases on traffic accidents and road geometric characteristics. For the acquisition sessions, drivers of different ages and genders drove vehicles on two routes at different day hours in different weather conditions. POLIDriving contains around 18 h of driving data, more than 61k observations, and 32 attributes. Unlike the other related datasets that include information on vehicle and road conditions, POLIDriving also includes information on the driver, weather conditions, traffic accidents, and road geometric characteristics. The dataset was tested in learning models to predict the risk levels of suffering a traffic accident. Hence, we built two learning models: Gradient Boosting Machine (GBM) and Multilayer Perceptron (MLP). GBM reached an accuracy value of 95.6%, and MLP reached an accuracy of 98.6%. Undoubtedly, POLIDriving will contribute greatly to the research on traffic accident prevention by providing a novel, numerous, and diverse driving dataset.