Improved Accuracy of Calculation of Vehicle Crash Severity in Highways using Random Forest over Decision Tree Algorithm

Authors

  • Vignesh.S
  • Sashi rekha K

DOI:

https://doi.org/10.47750/pnr.2022.13.S04.176

Keywords:

crash severity , Novel Random Forest Algorithm, Decision Tree Algorithm , Machine Learning, Artificial Intelligence,

Abstract

Aim: To improve the accuracy rate of vehicle crash severity in highways using Random forest over Decision tree.

Materials and Methods: Random forest and Decision tree with sample size of (N=10) is executed with varying training and testing splits
for calculating the accuracy for accident crash severity with g power as 75%, threshold 0.000 and confidence interval 95%. The performance of the classifiers are evaluated based on their accuracy rate using accident severity dataset.

Results: The accuracy for calculating accident crash severity in Random Forest(91%) and Decision Tree (85%) is obtained(P<0.005).

Conclusion: Prediction of accident crash severity using Random Forest (RF) algorithm appears to be significantly better than Decision Tree (DT) with improved accuracy.

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Published

2022-10-07

Issue

Vol. 13 SPECIAL ISSUE 04 (2022)

Section

Articles

How to Cite

Improved Accuracy of Calculation of Vehicle Crash Severity in Highways using Random Forest over Decision Tree Algorithm. (2022). Journal of Pharmaceutical Negative Results, 1471-1478. https://doi.org/10.47750/pnr.2022.13.S04.176