| Title |
Analysis of Accident Causes Based on the Severity of Personal Mobility Device Injuries
|
| Authors |
노정두(Noh, Jeongdu);오석진(Oh, Seokjin);서혁(Seo, Hyeok) |
| DOI |
https://doi.org/10.12652/Ksce.2026.46.1.0077 |
| Keywords |
개인형이동장치; 상관계수; 로지스틱 회귀분석; XGBoost; 변수 중요도 Personal mobility; Correlation coefficient; Logistic regression; XGBoost; Feature importance |
| Abstract |
This study was conducted to identify the characteristics of traffic accidents involving Personal Mobility (PM) devices, which have recently emerged as a major mode of transportation in urban areas, and to determine the primary causes affecting accident severity. To this end, a total of 469 PM accident cases that occurred in Gwangju Metropolitan City from 2021 to 2023 were collected, and a multifaceted analysis was performed using Pearson correlation analysis, Logistic Regression, and the XGBoost algorithm. The analysis results showed that both the number of accidents and accident severity, calculated using the Equivalent Property Damage only method, exhibited an increasing trend each year. Statistically, injury severity was found to have the strongest correlation with road type. Furthermore, predictive models were constructed and compared by applying various ratios of training and test data; the results indicated that the XGBoost model demonstrated superior predictive performance over the Logistic Regression model in most evaluation metrics, including AUC, precision, and recall. In the analysis of variable importance, accident type and traffic law violations were identified as critical factors in the Logistic Regression model, whereas the driver’s age was found to be the most influential factor affecting accident severity in the XGBoost model. The findings of this study quantitatively present the rick factors of PM accidents and can serve as effective fundamental data for establishing customized traffic safety policies by local governments and for promoting a safety culture among users in the future. |