# POLIDriving: A Public-Access Driving Dataset for Road Traffic Safety Analysis
## 5 Discussion  
Although several driving datasets are available, most target the autonomous driving area, and the rest are very limited in terms of the reduced number of data sources and attributes, not to mention that the best ones are not free public access. This fact motivated the creation of a new dataset. As mentioned, this work aimed to create a driving dataset that targets the driving behavior area and correlates and integrates information from heterogeneous sources. Thus, we created a public driving dataset, which we named POLIDriving. In comparison with related datasets in which the vehicles used in the acquisition sessions were equipped with high-resolution cameras, LIDAR radars, long-rage radars, OBD-II scanners, GNSS receivers, and IMU devices, in our dataset, the vehicles were equipped with an acquisition module which consists of a GNSS receiver, an OBD-II scanner, and a smartphone. This option was adopted because POLIDriving is intended to focus on driving behavior, not autonomous driving applications. Finally, although adding advanced equipment to vehicles to improve the dataset sounds tempting, a dataset that includes information from fully equipped vehicles is not viable because of resource limitations and mainly because such a dataset is beyond the aim of this study. In numbers, POLIDriving includes information from five heterogeneous data sources in contrast to two data sources of the related datasets. Similarly, POLIDriving can have more or less the same number of attributes (around 40) as the related datasets; however, its attributes are not from only one or two sources. Instead, POLIDriving has 13 attributes related to vehicle data, three to driver's data, 13 to weather data, two to traffic accidents, and one to road geometric characteristics. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Hour** & **Speed** & **rpm** & **Accel. ** & **Throttle** & **Eng. ** & **Head** & **Heart** & **Curr. ** & **Visib. ** & **Precip. ** & **Acdnt. ** & **Design** & **Acdnt. ** & **That** & **Risk Level** \\ \hline  
As mentioned above, POLIDriving focuses on driving behavior so that it could be used, for instance, in applications related to identifying risky driving behaviors in drivers. Therefore, having information from different drivers is desirable in those applications. In accordance with this, we recruited many drivers to participate in the acquisition sessions. In comparison with the rest of the related datasets, POLIDriving used more drivers, five to be exact, of different ages, genders, and driving experience. Finally, we decided that every driver should use the vehicle that drives daily to avoid unusual driving behaviors. Something to consider is how driving behavior influences the risk of suffering a traffic accident, as well as, how prone a driver with aggressive driving behavior is to accidents. According to the United Nations Economic Commission for Europe (UNECE), typical aggressive driving behavior includes speeding, not respecting traffic signals, or changing lanes inappropriately. Therefore, looking for more evidence confirming that aggressive driving behavior is closely related to a high probability of accidents is unnecessary. In this way, we added synthetic data for an unreal driver (furious) based on a real drive to POLIDriving. This driver is speeding, driving at very high rpm, and experiencing anxiety and stress, reflected by a very high heart rate. Once POLIDriving was released, it was tested in a model to predict risk levels of suffering traffic accidents. We tried combining semi-supervised and ensemble learning techniques for data labeling. It allowed us to label 91.5% of observations using only 8.5% of labeled observations with an accuracy of 82.0%. This result is a great achievement, considering the accuracies of 71.0% and 75.0% obtained by the label propagation and spreading methods. The other great achievement was the accuracies of 95.6% and 98.6% obtained by the supervised learning models.