## 2 Related Work
The ANNs and classification algorithms most frequently used in the related work were RF and MLPNN, followed by CNN, RBFNN, and LSTM. These are the most commonly used approaches for developing models that predict the risk of traffic accidents. Figure 2 presents the distribution of the used models in the related work analyzed. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors** & **Neural Networks** & **Purpose** & **Accuracy/Results** & **Predicted Classes** \\ \hline [PERSON] et al. [28] & GRNN (FFNN and RBFNN) and SOM & Present a hybrid predictive model for estimating the risk of road accidents. & Accuracy: 90.74\% & 2 \\ \hline [PERSON] and [PERSON] [29] & MLP & Propose a risk prediction MLP model that reflects the road’s static and dynamic features: length, speed limit, traffic volume, altitude, and azimuth of the sun. & Accuracy: 75\%, Precision: 73\%, Recall: 81\% & 4 \\ \hline [PERSON] et al. [30] & SVM, RF, XGBClassiffer, MLPNN & Conduct experiments with various machine learning algorithms to determine the best classifier for identifying severe or non-severe accident risks associated with Brazilian federal road hotspots. & Accuracy: SVM 58.60\%, RF 70.12\%, XGBClassiffer & 2 \\ \hline [PERSON] and [PERSON] [31] & SVM, LR, MLP, CNN-DNN & Propose a system based on deep learning to predict traffic accidents in urban environments and estimate the associated risk levels. & Accuracy: SVM 58.60\%, RF 70.12\%, XGBClassiffer & 71.25\%, MLPNN & 83\% \\ \hline [PERSON] and [PERSON] [31] & SVM, LR, MLP, CNN-DNN & Propose a system based on deep learning to predict traffic accidents in urban environments and estimate the associated risk levels. & Accuracy: SVM 58.60\%, LR 88\%, MLP & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 1: Number of related works that use the most common data sources. The analyzed studies also showed that CNN and MLPNN achieved the highest accuracy of 93% and 90%, respectively, while RBFNN, RF, and LSTM achieved an accuracy of 84.14%, 83.42%, and 65%, respectively. This evidence suggests that CNN and MLPNN perform better than other ANNs in predicting risk traffic accidents. Figure 3 displays the accuracy of all models used in the related work. Half of the models in the related work used binary classification, while the other half used multiclass classification; it is important to note that the binary classification models yielded better results than the multiclass classification models. The accuracy may decrease when the number of predictor classes increases. Figure 4 depicts the relationship between the type of classification and the accuracy obtained in the related work. Figure 4: Accuracy according to the number of predictor classes. Figure 3: Accuracy obtained by ANNs and algorithms in related work. Figure 2: ANNs and algorithms commonly used in related work. This study aimed to find an algorithm configuration that reduces the time required for validation and processing when working with this dataset type. Therefore, it was necessary to develop DL networks using CNNs and RFs. A previous study [19] showed that these approaches have achieved the best performance and accuracy in predicting accident risk levels. The performance of all models, including MLPNNs, should be compared to affirm if RBF networks can achieve similar or better results than other networks.