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Title: :  Road Traffic Accident Severity Prediction Using Machine Learning
PaperId: :  24451
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 3 2024
DUI:    16.0415/IJARIIE-24451
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
ArvindAMC Engineering College
DR.M.CHARLES AROCKIARAJAMC Engineering College

Abstract

Computer Engineering
Road traffic accidents, Severity prediction, Accident analysis, Machine learning, Predictive modeling, Traffic safety, Risk assessment, Data mining, Feature engineering, Decision trees, Random forest, Neural networks, Crash severity, Injury prediction, Traffic collision, Accident classification, Feature selection, Pattern recognition, Predictive analytics, Accident investigation
This paper delivers into the significant research road accidents represent a global challenge resulting in fatalities, injuries, and significant economic losses. Various countries and international bodies have implemented technologies, systems, and policies aimed at accident prevention. The integration of big traffic data and artificial intelligence holds promise for predicting and mitigating accident risks. Existing research predominantly explores the influence of road geometry, environmental conditions, and weather on accidents, often neglecting crucial human factors such as alcohol and drug use, age, and gender, which significantly impact accident severity. This study addresses these factors comprehensively, employing a range of single and ensemble machine learning (ML) methods to predict accident severity. Comparative analysis reveals that Random Forest (RF) consistently outperforms logistic regression (LR), K-nearest neighbor (KNN), naive Bayes (NB), extreme gradient boosting (XG Boost), and adaptive boosting (AdaBoost), achieving accuracy rates of 86.64% for binary and 67.67% for multiclass classifications. Ensemble methods generally surpass single-mode ML techniques in accuracy, with RF, XG Boost, and AdaBoost demonstrating superior performance. These findings contribute valuable insights into accident factors and severity assessment, offering potential applications in traffic safety and risk management.

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IJARIIE Arvind, and DR.M.CHARLES AROCKIARAJ. "Road Traffic Accident Severity Prediction Using Machine Learning" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 3 2024 Page 6400-6402
MLA Arvind, and DR.M.CHARLES AROCKIARAJ. "Road Traffic Accident Severity Prediction Using Machine Learning." International Journal Of Advance Research And Innovative Ideas In Education 10.3(2024) : 6400-6402.
APA Arvind, & DR.M.CHARLES AROCKIARAJ. (2024). Road Traffic Accident Severity Prediction Using Machine Learning. International Journal Of Advance Research And Innovative Ideas In Education, 10(3), 6400-6402.
Chicago Arvind, and DR.M.CHARLES AROCKIARAJ. "Road Traffic Accident Severity Prediction Using Machine Learning." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 3 (2024) : 6400-6402.
Oxford Arvind, and DR.M.CHARLES AROCKIARAJ. 'Road Traffic Accident Severity Prediction Using Machine Learning', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, 2024, p. 6400-6402. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Road_Traffic_Accident_Severity_Prediction_Using_Machine_Learning_ijariie24451.pdf (Accessed : ).
Harvard Arvind, and DR.M.CHARLES AROCKIARAJ. (2024) 'Road Traffic Accident Severity Prediction Using Machine Learning', International Journal Of Advance Research And Innovative Ideas In Education, 10(3), pp. 6400-6402IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Road_Traffic_Accident_Severity_Prediction_Using_Machine_Learning_ijariie24451.pdf (Accessed : )
IEEE Arvind, and DR.M.CHARLES AROCKIARAJ, "Road Traffic Accident Severity Prediction Using Machine Learning," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, pp. 6400-6402, May-Jun 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Road_Traffic_Accident_Severity_Prediction_Using_Machine_Learning_ijariie24451.pdf [Accessed : ].
Turabian Arvind, and DR.M.CHARLES AROCKIARAJ. "Road Traffic Accident Severity Prediction Using Machine Learning." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 3 ().
Vancouver Arvind, and DR.M.CHARLES AROCKIARAJ. Road Traffic Accident Severity Prediction Using Machine Learning. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(3) : 6400-6402. Available from: https://ijariie.com/AdminUploadPdf/Road_Traffic_Accident_Severity_Prediction_Using_Machine_Learning_ijariie24451.pdf
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