Characterizing road accidents in urban areas of Bogota (Colombia): A data science approach

Abstract

This paper analyzes a data set that contains reports of road accidents reported by the Municipal government of Bogota city, which occurred between 2016 and 2017, by using descriptive analysis and rule-model algorithms. The main objective is to characterize the road accident data and to highlight the variables that show a significant impact on the type of road accident. The results obtained shows the increase of road accidents by rush hour, from 6:00 to 8:00, one minor group from 12 m to 15:00 and another main group, from 17:00 to 19:00. Also, considering the day of the week, the days with the highest traffic accident frequency were Tuesday, Friday and Saturday. The rules-model obtained allowed to find out that the variables hour, day of week, locality and road geometry show a meaningful effect on the type of traffic accident; the model also allows to infer that the weather conditions does not pose a significant impact on assessing the type of traffic accident. The proposed rule model can be useful to propose accident prevention campaigns and to be incorporated into a traffic control system.