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Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: Case studies from Malaysian Federal Roads

机译:探索道路特征对正面碰撞的频率和严重性的影响:马来西亚联邦道路的案例研究

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摘要

Head-on crashes are among the most severe collision types and of great concern to road safety authorities. Therefore, it justifies more efforts to reduce both the frequency and severity of this collision type. To this end, it is necessary to first identify factors associating with the crash occurrence. This can be done by developing crash prediction models that relate crash outcomes to a set of contributing factors. This study intends to identify the factors affecting both the frequency and severity of head-on crashes that occurred on 448 segments of five federal roads in Malaysia. Data on road characteristics and crash history were collected on the study segments during a 4-year period between 2007 and 2010. The frequency of head-on crashes were fitted by developing and comparing seven count-data models including Poisson, standard negative binomial (NB), random-effect negative binomial, hurdle Poisson, hurdle negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. To model crash severity, a random-effect generalized ordered probit model (REGOPM) was used given a head-on crash had occurred. With respect to the crash frequency, the random-effect negative binomial (RENB) model was found to outperform the other models according to goodness of fit measures. Based on the results of the model, the variables horizontal curvature, terrain type, heavy-vehicle traffic, and access points were found to be positively related to the frequency of head-on crashes, while posted speed limit and shoulder width decreased the crash frequency. With regard to the crash severity, the results of RECOPM showed that horizontal curvature, paved shoulder width, terrain type, and side friction were associated with more severe crashes, whereas land use, access points, and presence of median reduced the probability of severe crashes. Based on the results of this study, some potential countermeasures were proposed to minimize the risk of head-on crashes.
机译:正面碰撞是最严重的碰撞类型之一,并且受到道路安全机构的高度关注。因此,它有理由付出更多的努力来减少这种碰撞类型的频率和严重性。为此,必须首先识别与碰撞发生相关的因素。这可以通过开发将碰撞结果与一组促成因素相关的碰撞预测模型来完成。这项研究旨在确定影响在马来西亚5条联邦道路的448个路段上发生的正面碰撞的频率和严重性的因素。在2007年至2010年的4年中,在研究阶段收集了有关道路特征和碰撞历史的数据。通过开发和比较包括Poisson,标准负二项式(NB)在内的七个计数数据模型,拟合了正面碰撞的频率。 ),随机效应负二项式,障碍泊松,障碍负二项式,零膨胀泊松和零膨胀负二项式模型。为了模拟碰撞严重程度,假设发生正面碰撞,则使用随机效应广义有序概率模型(REGOPM)。关于碰撞频率,根据拟合优度,发现随机效应负二项式(RENB)模型优于其他模型。根据模型的结果,发现变量水平曲率,地形类型,重型车辆交通和出入点与正面碰撞的频率呈正相关,而张贴的速度限制和路肩宽度降低了碰撞频率。关于碰撞严重程度,RECOPM的结果表明,水平曲率,铺砌的肩宽,地形类型和侧面摩擦与更严重的碰撞相关,而土地使用,进入点和中位数的存在降低了严重碰撞的可能性。根据这项研究的结果,提出了一些潜在的对策,以最大程度地减少正面碰撞的风险。

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