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Risk Assessment of Commercial dangerous -goods truck drivers using geo location data: A case study in China

机译:商业危险风险评估 - 使用地理位置数据的卡车司机:中国案例研究

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摘要

The primary objective of this study is to understand the relationship between driving risk of commercial dangerous-goods truck (CDT) and exposure factors and find a way to evaluate the risk of specific transportation environment, such as specific transportation route. Due to increasing transportation demand and potential threat to public, commercial dangerous goods transportation (CDGT) has drawn attention from decision makers and researchers within governmental and non-governmental safety organization. However, there are few studies focusing on driving risk assessment of commercial dangerous-goods truck by environmental factors. In this paper we employ survival analysis methods to analyze the impact of risk exposure factors on non-accident mileage of commercial dangerous-good truck and assess risk level of specific driving environment. Using raw location data from six transportation companies in China, we derive a set of 17 risk exposure factors that we use for model parameters estimation. The survival model and hazard model were estimated using the Weibull distribution as the baseline distribution. The results show that four factors - weather, traffic flow, travel time and average velocity have a significant impact on the non-accident mileage of driver in this company, and the assessment results of survival function and hazard function are robust to the different levels of testing data. The employment time has some effect on the results but does not result in a significant difference in most cases, and the task stability has little impact on the results. The findings of this study should be useful for decision makers and transportation companies to better risk assessment of CDT.
机译:本研究的主要目的是了解商业危险货物卡车(CDT)和曝光因素的驾驶风险之间的关系,并找到一种评估特定运输环境的风险的方法,例如特定的运输路线。由于运输需求增加和对公众的潜在威胁,商业危险品运输(CDGT)引起了政府和非政府安全组织内的决策者和研究人员的关注。然而,很少有研究专注于通过环境因素驾驶商业危险货物卡车的风险评估。在本文中,我们采用生存分析方法来分析风险暴露因子对商业危险良好卡车的非事故人寿的影响,并评估特定驾驶环境的风险水平。使用来自中国的六家运输公司的原始地点数据,我们推出了一组37个风险暴露因子,我们用于模型参数估计。使用Weibull分布作为基线分布估计生存模型和危险模型。结果表明,四个因素 - 天气,交通流量,旅行时间和平均速度对本公司的司机的非事故里程产生了重大影响,并且生存函数和危险功能的评估结果对不同的水平具有强大的测试数据。就业时间对结果产生了一些影响,但在大多数情况下不会导致显着差异,并且任务稳定性对结果影响不大。本研究的结果应对决策者和运输公司有用,以更好地对CDT进行风险评估。

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