# POLIDriving: A Public-Access Driving Dataset for Road Traffic Safety Analysis
## 6 Conclusions  
We obtained a public driving dataset that targets driving behavior, specifically road traffic safety, and stands out for its heterogeneity. Our non-expensive and easy installation acquisition agent allowed us to use different types of vehicles in the acquisition sessions. Thus, we could engage more drivers and their vehicles to avoid unusual driving behaviors. The lack of driving datasets with the heterogeneity feature motivated us to create a dataset with as many different sources as possible. Thus, we also integrated data from external databases and web services related to traffic accidents, road geometric characteristics, and weather conditions. Once built, the POLIDriving dataset allowed us to design and test learning models for road traffic safety. We tested the built dataset with our designed model to predict risk levels of suffering an accident. As you know, the performance of a learning model depends largely on the quality of the dataset used to train and test the model. The results confirmed, therefore, the good quality of POLIDriving, which also made us think that other authors will use our dataset in their applications. Undoubtedly, the POLIDriving dataset will greatly contribute to research on road traffic safety and will be a great asset to the community. However, our dataset is not without its limitations, notably in the representation of gender and age demographics among participants and the variety of driving conditions tested. Future enhancements will address these gaps by incorporating a more balanced participant pool and designing studies that simultaneously analyze driving behaviors across diverse routes. We strongly advocate for the continued expansion and refinement of POLIDriving. The dataset can offer even deeper insights into driving behaviors and traffic safety by including broader demographic and situational diversity. We invite the research community to explore POLIDriving for their projects, believing that collaborative efforts will propel forward our shared goal of improving road safety. Conceptualization, P.M.; methodology, P.M. and C.A.-A. ; investigation, P.M.; writing--original draft preparation, P.M.; writing--review and editing, P.M., A.L.V.C., S.S.-G. and M.H.-A. ; supervision, A.L.V.C., S.S.-G. and M.H.-A. All authors have read and agreed to the published version of the manuscript. This research was funded by Escuela Politecnica Nacional grant number PIS 22-20 (Development of learning models to predict risk levels of suffering traffic accidents using AI and ML and its application in a system of alerts for mobile devices). Not applicable. The dataset presented in this work is publicly available at [[https://github.com/laboratorioAI/polidriving](https://github.com/laboratorioAI/polidriving)]([https://github.com/laboratorioAI/polidriving](https://github.com/laboratorioAI/polidriving)) (accessed on 27 April 2024). Our recognition to VIIV (Vicerrectorado de Investigacion, Innovacion y Vinculacion) of Escuela Politecnica Nacional. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript:  
LiDAR Light Detection and Ranging GNSS Global Navigation Satellite System OBD On-Board Diagnostic IMU Inertial Measurement Unit CAN Controller Area Network LP Label Propagation LS Label Spreading SVM Support Vector Machine MLP Multilayer Perceptron RF Random Forest GBM Gradient Boosting Machine SMOTE Synthetic Minority Over-Sampling Technique