도심부 차대차 사고 잠재계층분석 및 심각도 모형 평가

초록

With the rapid growth of cities and increasing vehicle penetration, safety in urban areas has become an important social issue. In particular, 4-5 lane section shows a complex pattern due to various road components such as intersections, and tends to increase the severity of crashes due to high driving speeds. In addition, various factors such as increased traffic volume during peak hours and frequent lane changes due to staggered sections increase the risk of crashes. Considering these complex factors, this study aims to analyze the main factors influencing the severity of 4-5 lane urban road crashes. A total of 212 vehicle-to-vehicle crash cases were collected from dashcam footage on 4-5 lane urban dual carriageways across the country in 2021. Latent Class Analysis was conducted to capture latent heterogeneity in crash patterns. Additionally, to address data shortage, the dataset was augmented using the Random Over- Sampling Examples method. Subsequently, both the Binary Logit Model and the Random Parameter Binary Logit Model were estimated to analyze the factors influencing crash severity. The analysis identified two primary latent classes: Class 1 was dominated by rear-end crashes occurring on snowy or wet road surfaces, while Class 2 was characterized by side-impact crashes occurring under clear weather and dry surface conditions. Key determinants of crash severity included type of crash, road surface condition, and time period. Notably, both models indicated that rainy weather conditions were associated with elevated predicted crash severity, and wet and snowy road surfaces also showed significant associations with increased severity. Furthermore, the Random Parameter model outperformed the fixed-effect model across all performance metrics indicating that accounting for unobserved class yields more effective insights into crash severity. These findings are expected to contribute to the development of targeted safety strategies aimed at improving the safety of urban 4-5 lane roadways.

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