Open AccessAnalysing Road Accident Criticality using Data mining
Author(s) -
Shahsitha Siddique,
Nithin Ramakrishnan
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit1953138
Subject(s) - naive bayes classifier , computer science , decision tree , accident (philosophy) , road accident , classifier (uml) , criticality , road traffic accident , data mining , traffic accident , machine learning , bayesian probability , artificial intelligence , road traffic , support vector machine , transport engineering , engineering , philosophy , physics , epistemology , nuclear physics
Road transport is one of the most vital forms of transportation system, connecting both long and short distances in our country. There are several attributes, which affect the intensity of a road accident like speed of the vehicle, road conditions, time of the accident etc. Analysing these attributes gives an idea about the factors lead to the severity of the accident. Data mining is a method to analyse huge amount of traffic data in an efficient manner, which gives the factors, affect the road accidents. Several machine learning algorithms can be used to find the relation between traffic attributes the lead to the severity of the accidents. In this work, we use three methods for predicting accident criticality. First, Naive Bayesian Classifier is used to get the accident severity based on Bayes rule. Then, Decision Tree classifier is used for same purpose for accident severity calculation. Finally K-Nearest Neighbour(KNN) classifier is employed for severity calculation. The accuracy of the algorithms are compared and it is found that KNN performs better than the other two algorithms employed. The major aim of the work is to find the accident severity. Also the work aims to reduce road accidents by giving awareness to public using the above method.