Road Accident Analysis with Data Mining Approach: evidence from Rome
Antonio Comi, Antonio Polimeni, Chiara Balsamo
Abstract
Nowadays, road accident is one of the main causes of mortality worldwide. Then, measures are required to reduce or mitigate the accident impacts. The identification of the most effective measures requires an effective analysis of accidents able to identify and classify the causes that can trigger an accident. This study uses data mining as well as clustering approaches to analyze accident data of the 15 districts of Rome Municipality, collected from 2016 to 2019. The aim is to find out which data mining techniques are more suitable to analyze road accidents, to identify the most significant causes and the most recurrent patterns of road accidents by means of a descriptive analysis. Besides, a model to foresee road accidents is proposed. Results show that such analyses can be a powerful tool to plan suitable measures to reduce accidents as well as to forecast in advance the areas to be pointed out.