Machine learning approach on road accidents analysis in Calabarzon, Philippines: an input to road safety management

Abstract

This research was conducted to help the traffic policy makers and general public in preventing road incidents using the collected traffic accident dataset between the years 2016 and 2019. Data mining using classification algorithm was utilized to develop a predictive model for predicting occurrences of traffic accidents. Classification algorithms such as decision tree, k-nn, naïve bayes and neural network have been compared in identifying better classification capability in classifying stage of felony. Neural network shows a very promising result in classifying road accident with a total accuracy result of 87.63%. Nonetheless, k-nn and naïve bayes both acquired a higher than 80% accuracy which shows that this classification algorithms were also good in predicting road accidents. Moreover, public vehicle is more prone in accident rather than private vehicle in both stage of felony and accident may occur between or on 3:00pm and 6:00pm.