Analysis of Traffic Accident Features and Crash Severity Prediction

Abstract

Vehicle crashes occur because of numerous factors. It leads to loss of lives and permanent incapacity. The budgetary expenses of both individuals as well as for the nation are influenced by vehicle crashes. According to Road accident statistics, a total of 464910 road accidents were reported in India, claiming 1,47,913 lives and causing injuries to 4,70,975 persons every year. In this work, the UK data set sourced from Kaggle is used. For the study, 17 attributes and 35k records of the year 2015 are considered. The data set is imbalanced, so to balance out the data, the over-sampling technique is used. Random Forest, Decision tree, Logistic Regression, and Gradient Naïve Bayes algorithms are used to predict the severity of Accidents. To evaluate the model, performance measures like Accuracy, Precision, Recall, F1-Score are used. When Accuracy, Precision, F1-Score performance measure is considered Random Forest yielded the best result. When Recall performance measure is used, Random forest for Fatal, Decision Trees for Serious, Logistic regression for Slight yielded the best result.

Recognition of Air Passengers' Willingness to Pay for Seat Selection for Imbalanced Data Based on Improved XGBoost

“…Oversampling is to synthesize new minority class samples by some strategies to balance the number of positive and negative samples. The classical methods include SMOTE (Sindhu & George, 2022), SMOTE-Borderline (Han, Wang, & Mao, 2005), ADASYN (He, Yang, Garcia, & Li, 2008), and GAN-based methods (Lee & Park, 2021). However, the samples generated by artificial random replication may bring noise and reduce the classification accuracy of the model.…”