Traffic Accident Severity Rate Detection

Authors

  • Farha Parveen B.Tech. Student, Department of Computer Science and Engineering, Nalla Narasimha Reddy Education Society’s Group of Institutions, Hyderabad, India
  • Rahil Shaik B.Tech. Student, Department of Computer Science and Engineering, Nalla Narasimha Reddy Education Society’s Group of Institutions, Hyderabad, India
  • Shaik Raheem B.Tech. Student, Department of Computer Science and Engineering, Nalla Narasimha Reddy Education Society’s Group of Institutions, Hyderabad, India
  • Sarikonda Sree Hari Raju Associate Professor, Department of Computer Science and Engineering, Nalla Narasimha Reddy Education Society’s Group of Institutions, Hyderabad, India

Keywords:

XG Boost algorithm, Random Forest, SVM, Logistic Regression

Abstract

Road accident is most unwanted thing to happen to a road user, though they happen quite often. The most unfortunate thing is that we don't learn from our mistakes on road. Most of the road users are quite well aware of the general rules and safety measures while using roads but it is only the laxity on part of road users, which cause accidents and crashes. Main cause of accidents and crashes are due to human errors. The unbalance of traffic incident data has a great influence on the detection effect. Therefore, a traffic incident detection method based on factor analysis and weighted random forest (FA-WRF) is designed. We have used different Machine Learning Algorithms such as Random Forest algorithm, XG Boost algorithm, Linear Regression algorithm and Support Vector Machine algorithm to predict the severity rate of the accident whether it is slight or fatal or severe.

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Published

21-05-2023

Issue

Section

Articles

How to Cite

[1]
F. Parveen, R. Shaik, S. Raheem, and S. S. H. Raju, “Traffic Accident Severity Rate Detection”, IJRESM, vol. 6, no. 5, pp. 95–97, May 2023, Accessed: Dec. 30, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/2702