Analyzing Electrical Bikes Risk Factors Using Rough Set Theory and the Hybrid Logistic Regression Model

Abstract

The increasing popularity of electric bikes (e-bikes) has brought to light various risk factors associated with their use, necessitating a thorough analysis to enhance safety and reliability. This research paper aims to identify and evaluate the risk factors of e-bikes by employing Rough Set Theory (RST) and a Hybrid Logistic Regression Model. This research underscores the importance of comprehensive risk analysis for e-bikes and demonstrates the effectiveness of combining Rough Set Theory with logistic regression for predictive modeling. The findings of this study reveal that rider behavior, particularly compliance with traffic rules and use of safety gear, is the most influential factor in e-bike safety. The technical specifications of e-bikes, including battery performance and braking systems, were found to be critical in preventing accidents.