POLIDriving: A Public-Access Driving Dataset for Road Traffic Safety Analysis

Abstract

The problems with current driving datasets are their exclusivity to autonomous driving applications and their limited diversity in terms of sources of information and number of attributes. Thus, this paper presents a novel driving dataset that contains information from several heterogeneous sources and targets road traffic safety applications. We used an acquisition module based on software and hardware to collect information from a vehicle scanner and a health monitor. This module also consumes information from a weather web service and databases on traffic accidents and road geometric characteristics. For the acquisition sessions, drivers of different ages and genders drove vehicles on two routes at different day hours in different weather conditions. POLIDriving contains around 18 h of driving data, more than 61k observations, and 32 attributes. Unlike the other related datasets that include information on vehicle and road conditions, POLIDriving also includes information on the driver, weather conditions, traffic accidents, and road geometric characteristics. The dataset was tested in learning models to predict the risk levels of suffering a traffic accident. Hence, we built two learning models: Gradient Boosting Machine (GBM) and Multilayer Perceptron (MLP). GBM reached an accuracy value of 95.6%, and MLP reached an accuracy of 98.6%. Undoubtedly, POLIDriving will contribute greatly to the research on traffic accident prevention by providing a novel, numerous, and diverse driving dataset.