## 2 Related Work  
Building an effective traffic accident risk prediction system is important in traffic accident prevention. However, predicting the risk of a traffic accident is difficult because many related factors are involved [3]. For that reason, several types of research have been developed to predict the risk of traffic accidents. Table 1 shows an overview of the analyzed related work. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Neural Networks** & **Purpose** & **Accuracy/Results** & **Predicted Classes** \\ \hline [PERSON] [16] & CNN & Develop a machine learning approach using a CNN for predicting accident risk. & Precision: 93\%, Recall: 94\%, F1-score: 0.86 & 2 \\ \hline [PERSON] et al. [17] & Fuzzy-FARCHD, RBFNN, RF, NB, MLPNN & Compare different classifier algorithms using a dataset of crash injury categories. & Accuracy: Fuzzy-FARCHD 85.94\%, RBFNN 84.14\%, RF 83.42\%, NB 80.90\%, MLPNN 79.27\% & Accuracy: Accuracy: Fuzzy-FARCHD 85.94\%, RBFNN 84.14\%, RF 83.42\%, NB 80.90\%, MLPNN 79.27\% & Accuracy: 0.65 & 2 \\ \hline [PERSON] et al. [18] & DAP (LSTM) & Propose a deep neural network model called the DAP to predict the risk of a traffic incident. & F1-score: 0.65 & 2 \\ \hline [PERSON] et al. [19] & CNN, RF & Propose a traffic accident risk forecasting algorithm based on deep learning for edge-cloud internet of vehicles. & AUC: 0.9921 & 2 \\ \hline [PERSON] et al. [20] & CNN & Investigate the feasibility of utilizing deep learning models to identify and forecast crash occurrence. & Accuracy: 77.34\%, F1-score: 0.7651 & 2 \\ \hline [PERSON] et al. [21] & RF, FFNN & Develop a risk-level accident classifier using a deep learning method to predict high-risk taxi drivers. & Accuracy: 86\%, F1-score: 0.77 & 3 \\ \hline [PERSON] et al. [22] & LSTM, CNN & Propose a real-time crash risk prediction model for arterial using LSTM and a CNN. & Sensitivity: LSTM 80\%, CNN: 64\%, LSTM-CNN 88\% & 2 \\ \hline [PERSON] et al. [23] & RBF, RF, DFNN-model & Propose a model to predict the levels of risk for traffic accidents. & Accuracy: RBF 71.8\%, RF 73.2\%, DFNN-model 83\% & 3 \\ \hline [PERSON] et al. [24] & MLPNN, RF & Conduct a study on the classification of traffic accident risk in urban areas. Their objective was to propose effective traffic management solutions to minimize social losses in cities with high traffic volume. & Accuracy: MLPNN 89\%, RF 75.4\% & 4 \\ \hline [PERSON] et al. [25] & LR, RF, XGBoost, FFNN, LSTM & Evaluate machine learning risk assessment models: LR, RF, XGBoost, LSTM networks. & Accuracy: LR 75.2\%, RF 75.7\%, XGBoost 75.5\%, FFNN 75.4\%, LSTM 55.3\% & 2 \\ \hline [PERSON] et al. [26] & DNN, DBN, MLP, CNN & Develop a model to predict high-risk intersections for traffic accidents. & Accuracy: DNN 72.62\%, DBN 72.62\%, MLP 71.94\%, CNN 72.62\% & 3 \\ \hline [PERSON] et al. [27] & RF, XGBoost, SVM & Develop prediction models for traffic crash risk potential using traffic-related data. & Accuracy: RF 80\%, XGBoost 80\%, SVM 81\% & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative table of related work. The related works propose approaches that use either a static dataset or a heterogeneous source dataset. However, these studies do not incorporate new data on the fly to train or test new models. Additionally, most of these works do not present the time used for execution and obtaining results. The primary data sources used in the related work were traffic accidents, weather conditions, road infrastructure, drivers' data, and vehicles' data. Traffic accidents are the most commonly used data source for estimating the negative effects of traffic incidents. These data include the number of fatalities, injuries, and collisions resulting in casualties or fatalities. It should be noted that the number of attributes used varied from 6 to 42. Figure 1 presents the related work using the most common data sources.