Wrong-Lane Accidents Detection using Random Forest Algorithm in comparison with Decision Tree for Improved Accuracy

Abstract

Aim: The proposed study aims to perform detection of wrong-lane accidents using Decision Tree (DT) algorithm and compare accuracy with Random Forest (RF) algorithm. Materials and Methods: Decision Tree is applied on a road accident dataset that consists of 1834 records. A Machine learning technique for the detection of wrong-lane accidents which compares Decision Tree and Random Forest has been proposed and developed. Sample size was calculated as 21 in each group using G power. Sample size was calculated using clinical analysis, with alpha and beta values of 0, 05 and 0.5, 95% confidence, 80% pre-test power and enrolment ratio is 1. The accuracy of the detection of wrong-lane accidents was evaluated and recorded. Results: The accuracy was maximum in detection of wrong-lane accidents using Decision Tree (89.88%) with minimum mean error when compared with Random Forest (89.77%) and attained significance value of p = 0.02 (p<=0.05). Conclusion: The study proves that Decision Tree Algorithm exhibits better accuracy than Random Forest in detection of wrong-lane accidents