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A multivariate spatial crash frequency model for identifying sites with promise based on crash types

机译：用于基于崩溃类型识别具有承诺的站点的多元空间崩溃频率模型

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摘要

Many studies have proposed the use of a systemic approach to identify sites with promise (SWiPs). Proponents of the systemic approach to road safety management suggest that it is more effective in reducing crash frequency than the traditional hot spot approach. The systemic approach aims to identify SWiPs by crash type(s) and, therefore, effectively connects crashes to their corresponding countermeasures. Nevertheless, a major challenge to implementing this approach is the low precision of crash frequency models, which results from the systemic approach considering subsets (crash types) of total crashes leading to higher variability in modeling outcomes. This study responds to the need for more precise statistical output and proposes a multivariate spatial model for simultaneously modeling crash frequencies for different crash types. The multivariate spatial model not only induces a multivariate correlation structure between crash types at the same site, but also spatial correlation among adjacent sites to enhance model precision. This study utilized crash, traffic, and roadway inventory data on rural two-lane highways in Pennsylvania to construct and test the multivariate spatial model. Four models with and without the multivariate and spatial correlations were tested and compared. The results show that the model that considers both multivariate and spatial correlation has the best fit. Moreover, it was found that the multivariate correlation plays a stronger role than the spatial correlation when modeling crash frequencies in terms of different crash types. (C) 2015 Elsevier Ltd. All rights reserved.
机译：许多研究建议使用系统的方法来识别有希望的站点（SWiP）。支持道路安全管理的系统方法表明，与传统的热点方法相比，该方法在减少事故频率方面更为有效。该系统方法旨在通过崩溃类型识别SWiP，因此可以有效地将崩溃与其相应的对策联系起来。尽管如此，实现此方法的主要挑战是崩溃频率模型的精度低，这是由于系统性方法考虑了总崩溃的子集（崩溃类型）而导致建模结果具有更高的可变性。这项研究满足了对更精确的统计输出的需求，并提出了一个多元空间模型来同时为不同的碰撞类型建模碰撞频率。多元空间模型不仅在同一地点的碰撞类型之间引入了多元相关结构，而且在邻近地点之间也产生了空间相关性，从而提高了模型的精度。这项研究利用宾夕法尼亚州农村两车道高速公路上的碰撞，交通和道路库存数据来构建和测试多元空间模型。测试并比较了具有和不具有多元和空间相关性的四个模型。结果表明，同时考虑多元和空间相关性的模型具有最佳拟合。此外，还发现，当根据不同的碰撞类型对碰撞频率进行建模时，多元相关性比空间相关性更重要。 （C）2015 Elsevier Ltd.保留所有权利。

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