# POLIDriving: A Public-Access Driving Dataset for Road Traffic Safety Analysis
## 4 Results  
The POLIDriving dataset contains data from seven data acquisition sessions. Five drivers and four vehicles participated and were used in the sessions. Additionally, we generated two extra sessions with synthetic data based on real data. POLIDriving contains around 18 h of driving data and 32 attributes from five heterogeneous sources. All the raw and processed data files are stored in a public GitHub repository [22]. Table 7 presents a sample of the processed data file for the Alonso user for Route 1. To test POLIDriving, we selected the traffic accident prediction research area, specifically predicting risk levels by identifying risky driving patterns. In that way, we built two learning models, one based on neural networks and the other on decision trees. First of all, we performed data integration to join data from the Accuweather web service and the traffic accident and road geometric characteristics databases. Then, we performed data cleaning and feature selection over POLIDriving. It was reduced to 14 attributes out of 32 available ones. Low variance filters and correlation and mutual information matrices were used to select the most relevant attributes. Figure 4 presents the correlation matrix before and after the feature selection. Once preprocessing was performed, we manually labeled a very small portion of the observations. Then, we used semi-supervised techniques and a voting ensemble to label the rest. For manual labeling, we identified threshold values for the attributes and established ranges and their penalties. Table 8 presents a sample of threshold values and ranges used by experts for manual data labeling, and Table A1 in Appendix A presents the whole list of threshold values and ranges. We determined the risk level among low, medium, high, and very high depending on the number of penalties. Since few observations were labeled with high-risk levels, extra observations with synthetic data presenting risky driving patterns were added. It permitted labeling 8.5% of the total observations (23.152). From the total of labeled observations (1.980), we considered 75% (1.485) for training and 25% (495) for testing. Unlabeled observations (21.172) were labeled using a voting ensemble with labels generated by semi-supervised learning methods such as label propagation, label spreading, and self-training based on Multilayer Perceptron, Random Forest, and Gradient Boosting Machine. Table 9 presents the results of the labeling data. Despite the strategies applied to add observations, the dataset was still unbalanced, so we applied the oversampling technique known as SMOTE [23]. It helped to balance all the minority classes with the majority class. Thus, the dataset reached values of 12.839 for every risk level. Considering the most common algorithms used in prediction models for traffic accident prevention proposed by [2], we built two models; the first used a Gradient Boosting Machine (GBM) with 100 estimators, a learning rate of 0.1, a maximum depth of  
Figure 3: Control points on the map. 3, and relu as the activation function, and the second one used a Multilayer Perceptron (MLP) with three hidden layers, 100 neurons for each layer, and the hyperbolic tangent as the activation function. Table 10 presents the configurations for the learning models. Both models were trained and evaluated using cross-validation with ten folds. Table 11 presents the evaluation of the learning models. Finally, Table 12 presents a random sample of observations and their predicting classes. The observation marked with (*15) can be interpreted as follows. That observation received the risk level'very high' because the driver speeded at 81 km/h, exceeding the designated speed of 80 km/h. Her/his vehicle worked in normal conditions, with an engine temperature of 94 \({}^{\circ}\)C and at 2950 rpm, low of normal range. However, the driver presented a slight tachycardia (101 bpm), probably due to anxiety or stress, while driving in adverse rain conditions with low visibility (3.2 km) and precipitation of 8 mm.