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Article:A Perspective Analysis of Traffic Accident using Data Mining Techniques

by S.Krishnaveni, Dr.M.Hemalatha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 23 - Number 7
Year of Publication: 2011
Authors: S.Krishnaveni, Dr.M.Hemalatha
10.5120/2896-3788

S.Krishnaveni, Dr.M.Hemalatha . Article:A Perspective Analysis of Traffic Accident using Data Mining Techniques. International Journal of Computer Applications. 23, 7 ( June 2011), 40-48. DOI=10.5120/2896-3788

@article{ 10.5120/2896-3788,
author = { S.Krishnaveni, Dr.M.Hemalatha },
title = { Article:A Perspective Analysis of Traffic Accident using Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 23 },
number = { 7 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 40-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume23/number7/2896-3788/ },
doi = { 10.5120/2896-3788 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:09:34.501578+05:30
%A S.Krishnaveni
%A Dr.M.Hemalatha
%T Article:A Perspective Analysis of Traffic Accident using Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 23
%N 7
%P 40-48
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data Mining is taking out of hidden patterns from huge database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries in this world. Roadway patterns are useful in the development of traffic safety control policy. This paper deals with the some of classification models to predict the severity of injury that occurred during traffic accidents. I have compared Naive Bayes Bayesian classifier, AdaBoostM1 Meta classifier, PART Rule classifier, J48 Decision Tree classifier and Random Forest Tree classifier for classifying the type of injury severity of various traffic accidents. The final result shows that the Random Forest outperforms than other four algorithms.

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Index Terms

Computer Science
Information Sciences

Keywords

Data mining machine learning Naive Bayes Classifiers AdaBoostM1 PART J48 Random Forest

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