Real Time Efficient Accident Predictor System using Machine Learning Techniques (kNN, RF, LR, DT)
P. Tamije Selvy1, M. Ragul2, G. Naveen Vignesh3, M. Anitha4
1Dr. P. Tamijeselvy*, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, India.
2M. Ragul, Department of Computer Science and Engineering, Sri Krishna College of Technology,Coimbatore, India.
3G. Naveen Vignesh, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, India.
4M. Anitha, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, India.
Manuscript received on December 02, 2020. | Revised Manuscript received on December 05, 2020. | Manuscript published on December 30, 2020. | PP: 108-111 | Volume-10 Issue-2, December 2020. | Retrieval Number: 100.1/ijeat.D6910049420 | DOI: 10.35940/ijeat.D6910.1210220
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Abstract: Real time crash predictor system is determining frequency of crashes and also severity of crashes. Nowadays machine learning based methods are used to predict the total number of crashes. In this project, prediction accuracy of machine learning algorithms like Decision tree (DT), K-nearest neighbors (KNN), Random forest (RF), Logistic Regression (LR) are evaluated. Performance analysis of these classification methods are evaluated in terms of accuracy. Dataset included for this project is obtained from 49 states of US and 27 states of India which contains 2.25 million US accident crash records and 1.16 million crash records respectively. Results prove that classification accuracy obtained from Random Forest (RF) is96% compared to other classification methods.
Keywords: Machine Learning, Accident Prediction, classification Techniques
Scope of the Article: Machine Learning