Analysis of Risk Bus Driver Characteristics and Research on Risk Level Evaluation Methods for Bus Drivers

Abstract

Currently, there is a lack of a comprehensive and integrated method for assessing risk levels of bus drivers. This study utilizes XGBOOST and Logistic regression models to analyze the impact of various indicator features of bus drivers on crash risks. A grey whitening weight function model is then constructed to evaluate the risk levels of bus drivers, achieving a quantified assessment of their risk levels. Based on the research findings, the following observations were made: 1) The number of non-fault crashes is the most important risk feature influencing the occurrence of at-fault crashes; 2) Features related to crashes, violations, and alarms, as well as age, bus driving experience, driving experience, route length, and the number of stops, have a negative impact on the occurrence of at-fault crashes; 3) The study quantifies bus drivers into five risk levels, with higher levels indicating higher risk. It was found that 94.94% of bus drivers are in the second and third risk levels, 4.93% in the first and fourth risk levels, and only 0.12% of bus drivers are in the highest fifth risk level. The conclusions drawn in this study, along with the proposed method for evaluating risk levels of bus drivers, will contribute to the evaluation and management of bus drivers by bus companies and transportation authorities, thereby reducing crashes in public transportation.

Keywords

• Bus driver
• data mining
• machine learning
• data analytics
• level evaluation