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首页> 外文期刊>Journal of Transportation Engineering >Estimating Factors Contributing to Frequency and Severity of Large Truck-Involved Crashes
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Estimating Factors Contributing to Frequency and Severity of Large Truck-Involved Crashes

机译：估算与大型卡车相关的撞车事故的频率和严重程度的因素

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Understanding the factors that contribute to crash frequency and severity will assist better highway design and develop appropriate countermeasures for hot spots, thereby improving the safety of the road system. This study explores the influences of risk factors on frequency and severity of large truck-involved crashes. Multinomal logit (MNL) and negative binomial (NB) models are proposed to analyze crash severity and frequency, respectively. The explanatory factors include characteristics of the vehicles, drivers, traffic, environment, and roadway geometric design features. To obtain better parameter estimation results, the maximum likelihood (ML) method and Bayesian method are employed. The results show that the MNL and NB models have a better goodness of fit under the Bayesian estimation framework. Using Bayesian MNL and NB models, factors that significantly affect crash frequency and severity outcomes are analyzed. Some critical factors that contribute significantly to both crash severity and frequency estimation of large truck-involved crashes, including truck percentage, annual average daily traffic (AADT), driver condition, and weather condition, are identified and discussed. Driver age, speed limit, and location type are found to have significant effects only on the frequency of large truck-involved crashes. Seat belt usage, light condition, and terrain type are found to have significant effects only on the severity of large truck involved crashes. The results from this study will be valuable in transportation policy making, improvement of carrier operation, and crash-cost reduction. (C) 2017 American Society of Civil Engineers.
机译：了解导致事故频率和严重程度的因素，将有助于更好的高速公路设计并针对热点制定适当的对策，从而提高道路系统的安全性。这项研究探讨了危险因素对大型卡车事故的发生频率和严重性的影响。提出了多项式对数（MNL）模型和负二项式（NB）模型来分别分析碰撞严重性和频率。解释因素包括车辆，驾驶员，交通，环境和道路几何设计特征的特征。为了获得更好的参数估计结果，采用了最大似然法和贝叶斯方法。结果表明，在贝叶斯估计框架下，MNL和NB模型具有较好的拟合优度。使用贝叶斯MNL和NB模型，分析显着影响碰撞频率和严重性结果的因素。确定并讨论了一些重要因素，这些因素对撞车的严重程度和与卡车有关的大型撞车的频率估算均起重要作用，包括卡车百分比，年平均日流量（AADT），驾驶员状况和天气状况。发现驾驶员的年龄，速度限制和位置类型仅对大型卡车事故的发生频率有重大影响。发现安全带的使用，光线条件和地形类型仅对涉及卡车的大型撞车事故的严重程度具有重大影响。这项研究的结果将对制定交通政策，改善运输公司的运营以及降低撞车成本具有重要意义。 （C）2017年美国土木工程师学会。

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