Ramesh M Chakrasali1
, Naganandini G2
, Ancy Thomas3
Section:Research Paper, Product Type: Journal Paper
Volume-07 ,
Issue-14 , Page no. 380-385, May-2019
CrossRef-DOI:
https://doi.org/10.26438/ijcse/v7si14.380385
Online published on May 15, 2019
Copyright © Ramesh M Chakrasali, Naganandini G, Ancy Thomas . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Ramesh M Chakrasali, Naganandini G, Ancy Thomas, “Three Class Classification Technique To Predict Road Accident Severity,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.380-385, 2019.
MLA Style Citation: Ramesh M Chakrasali, Naganandini G, Ancy Thomas "Three Class Classification Technique To Predict Road Accident Severity." International Journal of Computer Sciences and Engineering 07.14 (2019): 380-385.
APA Style Citation: Ramesh M Chakrasali, Naganandini G, Ancy Thomas, (2019). Three Class Classification Technique To Predict Road Accident Severity. International Journal of Computer Sciences and Engineering, 07(14), 380-385.
BibTex Style Citation:
@article{Chakrasali_2019,
author = {Ramesh M Chakrasali, Naganandini G, Ancy Thomas},
title = {Three Class Classification Technique To Predict Road Accident Severity},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {14},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {380-385},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1159},
doi = {https://doi.org/10.26438/ijcse/v7i14.380385}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i14.380385}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1159
TI - Three Class Classification Technique To Predict Road Accident Severity
T2 - International Journal of Computer Sciences and Engineering
AU - Ramesh M Chakrasali, Naganandini G, Ancy Thomas
PY - 2019
DA - 2019/05/15
PB - IJCSE, Indore, INDIA
SP - 380-385
IS - 14
VL - 07
SN - 2347-2693
ER -
In recent years, road accidents are becoming more and more due to the larger growth in population. The growth of population and the increase in number of vehicles has led to a traffic congestion and sometimes may results in accidents. There are many factors that may lead to the road accidents and those maybe the driver’s carelessness, drunk and drive, road conditions etc. Using the technology, necessary measures can be taken in order to predict the accidents at prior and to prevent the occurrence of accidents. In this research paper we use Gretl tool to identify the factors that are significantly contributing to the accidents, applied the logistic regression classification technique to build the machine learning model in order to predict the accident severity using the predictors like number of vehicles involved, road conditions, weather conditions, light conditions etc. Here we consider the accident severity as a dependent variable which is of three classes that is slight, serious and fatal. The main objective of this paper is that the accident has already occurred, in which we are predicting the severity of that accident.
AccidentSeverity,Predictions,LogisticRegression,Gretl,Tableau
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[2] Maher Al-Zuhairi, Biswajeet Pradhan “Severity Prediction of Traffic Accidents with Recurrent Neural Networks” Article in Applied sciences
[3] SharafAlkheder, Madhar M. Taamneh, Salah Taamneh ”Traffic Accident Severity Prediction Using Artificial Neural Network” Journal of Forecasting,J.Forecast(2016) Published in Wiley Online Library.
[4] Rui Garrido, Ana Bastas, Ana de Almeida, Jose Paulo Elvas” Prediction of Road Accident Severity using the Ordered ProbitModel” ElseVier publication.
