Traffic injury prevention · 2025
Analyzing the severity of motorcycle single-vehicle crashes on rural roads under diverse lighting conditions.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wei Fulu, Sun Lizu, Guo Yongqing, Zhang Xin, Ma Yanyan, Guo Dong, Xu Yalin
Аннотация
OBJECTIVE: Motorcycle crashes are prevalent in rural areas of China. While the specific sequence of factors contributing to single-vehicle motorcycle crashes on rural roads in different lighting conditions has not yet been deeply revealed. A 3-year (2020-2022) crash database from Shandong Province, China, was used to explore the causal chain of motorcycle single vehicle collisions. The analysis focused on motorcycle single-vehicle collisions that occurred on rural roads, which were classified into three distinct lighting conditions: daylight, dark-with-streetlight, and dark-no-streetlight.
METHODS: Firstly, a random parameters approach with heterogeneity in means and variances (RPL-HMV) was constructed. It was used to explore the factors that influence crash severity, including rider characteristics, crash details, road conditions, environmental factors, and temporal aspects. Secondly, based on the output of RPL-HMV, the association rule mining method (ARM) was employed to conduct in-depth research on the chain of factors that affects the severity of crashes.
RESULTS: The results revealed that under daylight conditions, the majority of single-vehicle motorcycle collisions were associated with factors including rider age, road alignment, collision with fixtures (collision F), and visibility. Most crashes occurring in dark-with-streetlight conditions were attributed to factors such as alcohol influence, weather conditions, collision F, collision with non-fixed objects (collision NF), rider gender, and road alignment. The majority of crashes in dark-no-streetlight conditions were linked to factors such as road surface conditions, rollover incidents, and visibility limitations, all of which were contributing factors to the severity of the crashes.
CONCLUSIONS: The combination of ARM and RPL-HMV methods can capture the chain effect of crash causes, which helps to more fully restore the true scene of traffic crashes. Moreover, this study provides valuable guidance for policymakers and traffic safety experts in designing more effective safety measures.
METHODS: Firstly, a random parameters approach with heterogeneity in means and variances (RPL-HMV) was constructed. It was used to explore the factors that influence crash severity, including rider characteristics, crash details, road conditions, environmental factors, and temporal aspects. Secondly, based on the output of RPL-HMV, the association rule mining method (ARM) was employed to conduct in-depth research on the chain of factors that affects the severity of crashes.
RESULTS: The results revealed that under daylight conditions, the majority of single-vehicle motorcycle collisions were associated with factors including rider age, road alignment, collision with fixtures (collision F), and visibility. Most crashes occurring in dark-with-streetlight conditions were attributed to factors such as alcohol influence, weather conditions, collision F, collision with non-fixed objects (collision NF), rider gender, and road alignment. The majority of crashes in dark-no-streetlight conditions were linked to factors such as road surface conditions, rollover incidents, and visibility limitations, all of which were contributing factors to the severity of the crashes.
CONCLUSIONS: The combination of ARM and RPL-HMV methods can capture the chain effect of crash causes, which helps to more fully restore the true scene of traffic crashes. Moreover, this study provides valuable guidance for policymakers and traffic safety experts in designing more effective safety measures.