Traffic injury prevention · 2021
A skewed logistic model of two-unit bicycle-vehicle hit-and-run crashes.
СтатьяПоддержка исследования: вне правительства СШАНазвание 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.
Jiang Chenming, Tay Richard, Lu Linjun
Аннотация
OBJECTIVE: Hit-and-run behavior in crashes is a severe offense worldwide because the identification and emergency rescue of any injured road user is delayed. A motorist's run from the crash scene is especially serious for a cyclist who would be more prone to be physically injured in a bicycle-vehicle (BV) crash. The objective of this paper is to explore potential risk factors that contribute to the hit-and-run (HR) behavior of a driver after a two-unit BV collision.
METHODS: The data used in this study are extracted from traffic crash records in the city of Durham, North Carolina in 2007-2014. This study uses the skewed logistic (Scobit) model to account for the skewness of the dependent variable (i.e., HR) in the dataset.
RESULTS: The Likelihood ratio test, AIC and BIC results show that the Scobit model is preferred to the standard binary logistic model for modeling a driver's decision to run from a two-unit BV crash scene. Estimation results indicate that, the driver's tendency to run from a crash scene without reporting it in Durham increases if the bicyclist is a teenager or an adult, a drunk-driving or a speeding driver is involved, when the crash happens at night (19:00-6:59), on a local street, or when the automobile overtakes the bicycle. HR behavior will decrease if the cyclist is drunk, an SUV is involved, or the bicyclist fails to yield.
CONCLUSIONS: The findings of this study are important and useful when developing countermeasures to prevent BV-HR crashes and to improve cycling safety.
METHODS: The data used in this study are extracted from traffic crash records in the city of Durham, North Carolina in 2007-2014. This study uses the skewed logistic (Scobit) model to account for the skewness of the dependent variable (i.e., HR) in the dataset.
RESULTS: The Likelihood ratio test, AIC and BIC results show that the Scobit model is preferred to the standard binary logistic model for modeling a driver's decision to run from a two-unit BV crash scene. Estimation results indicate that, the driver's tendency to run from a crash scene without reporting it in Durham increases if the bicyclist is a teenager or an adult, a drunk-driving or a speeding driver is involved, when the crash happens at night (19:00-6:59), on a local street, or when the automobile overtakes the bicycle. HR behavior will decrease if the cyclist is drunk, an SUV is involved, or the bicyclist fails to yield.
CONCLUSIONS: The findings of this study are important and useful when developing countermeasures to prevent BV-HR crashes and to improve cycling safety.