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首页> 外文期刊>Journal of Safety Research >Application of machine learning technique for optimizing roadside design to decrease barrier crash costs, a quantile regression model approach
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Application of machine learning technique for optimizing roadside design to decrease barrier crash costs, a quantile regression model approach

机译:机床学习技术在优化路边设计下降低屏障碰撞成本,量化回归模型方法

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Introduction: In-transport vehicles often leave the travel lane and encroach onto natural objects on the roadsides. These types of crashes are called run-off the road crashes (ROR). Such crashes accounts for a significant proportion of fatalities and severe crashes. Roadside barrier installation would be warranted if they could reduce the severity of these types of crashes. However, roadside barriers still account for a significant proportion of severe crashes in Wyoming. The impact of the crash severity would be higher if barriers are poorly designed, which could result in override or underride barrier crashes. Several studies have been conducted to identify optimum values of barrier height. However, limited studies have investigated the monetary benefit associated with adjusting the barrier heights to the optimal values. In addition, few studies have been conducted to model barrier crash cost. This is because the crash cost is a heavily skewed distribution, and well-known distributions such as linear or poison models are incapable of capturing the distribution. A semi-parametric distribution such as asymmetric Laplace distribution can be used to account for this type of sparse distribution. Method: Interaction between different predictors were considered in the analysis. Also, to account for exposure effects across various barriers, barrier lengths and traffic volumes were incorporated in the models. This study is conducted by using a novel machine-learning-based cost-benefit optimization to provide an efficient guideline for decision makers. This method was used for predicting barrier crash costs without barrier enhancement. Subsequently the benefit was obtained by optimizing traffic barrier height and recalculating the benefit and cost. The trained model was used for crash cost prediction on barriers with and without crashes. Results: The results of optimization clearly demonstrated the benefit of optimizing the heights of road barriers around the state. Practical Applications: The findings can be utilized by the Wyoming Department of Transportation (WYDOT) to determine the heights of which barriers should be optimized first. Other states can follow the procedure described in this paper to upgrade their roadside barriers. (c) 2021 National Safety Council and Elsevier Ltd. All rights reserved.
机译:简介:运输车辆经常离开行程车道并侵占道路上的天然物体。这些类型的崩溃被称为漫步道路崩溃(ROR)。这种崩溃占致命比例和严重的崩溃。如果他们可以减少这些类型的崩溃的严重程度,将保证路边屏障安装。然而,路边障碍仍然占怀俄明队中的严重崩溃比例。如果设计障碍物很差,则碰撞严重程度的影响将更高,这可能导致覆盖或低劣的屏障崩溃。已经进行了几项研究以确定屏障高度的最佳值。然而,有限的研究已经调查了与调节屏障高度与最佳值相关的货币效益。此外,还有很少的研究以模拟屏障撞击成本。这是因为崩溃成本是一个较大的倾斜分布,并且众所周知的分布如线性或毒物模型是无法捕获分布的。可以使用诸如非对称LAPLACE分布的半参数分布来解释这种类型的稀疏分布。方法:在分析中考虑了不同预测因子之间的相互作用。此外,为了考虑各种屏障的曝光效果,在模型中纳入了屏障长度和流量体积。本研究是通过使用基于新的机器学习的成本效益优化进行,为决策者提供有效的准则。该方法用于预测无障碍增强的屏障碰撞成本。随后通过优化交通障碍高度并重新计算益处和成本来获得益处。培训的模型用于对壁垒的崩溃成本预测,无崩溃。结果:优化结果清楚地表明了优化国家道路壁垒的高度。实际应用:调查结果可由Wyoming运输部(Wydot)利用,以确定应首先优化障碍的高度。其他州可以遵循本文描述的程序以升级其路边障碍。 (c)2021国家安全委员会和elestvier有限公司保留所有权利。

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