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Investigating transferability of two different approaches for estimating safety performance of motorways

机译:调查两种不同方法估算高速公路安全性能的可转移性

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The analysis of a road network could be carried out using different techniques, the most advanced of which for the identification of hazardous sites are based on a statistical tool known as a “Safety Performance Function” (SPF). SPFs are fundamental to procedures in the Highway Safety Manual (HSM) and Safety Analyst, both of which require calibration of the SPFs to local conditions. In the case of infrastructure in which the traffic flow is physically divided, such as motorways, there are two different methods to calibrate an SPF; one is based on a consideration of two directional segments, the other one is based on mono-directional segments with a correction factor that takes into account the presence of two directional traffic, as proposed by Safety Analyst. In the latter case, traffic flows are the sum of traffic flows for each direction. The two methods give a different calibration factor for local condition, i.e., different performance in terms of SPF transferability. This paper seeks to address these differences by investigating the two approaches to calibrate SPFs using a motorway sample data from Italy and focusing on a comparison in terms of model transferability. The goodness of fit of the models is investigated using CURE plots and the R~2 values. The two sets of models give good results in terms of goodness of fit but they have different values for the calibration factor. The calibration factor of the mono-directional model is closer to a value of one, suggesting a better performance in terms of transferability.
机译:对道路网络的分析可以使用不同的技术进行,其中最先进的用于识别危险场地是基于称为“安全性能”(SPF)的统计工具。 SPFS是高速公路安全手册(HSM)和安全分析师的过程的基础,两者都需要将SPF校准到当地条件。在基础设施的情况下,交通流量物理地划分,例如高速公路,有两种不同的方法来校准SPF;一个是基于两个定向段的考虑,另一个是基于单向段,校正因子考虑到安全分析师提出的两个方向性交通的存在。在后一种情况下,交通流量是每个方向的流量流量。这两种方法为本地条件提供了不同的校准因子,即在SPF可转换性方面的不同性能。本文旨在通过调查使用来自意大利的高速公路样本数据来校准SPF的方法并在模型可转换性方面进行比较来解决这些差异。使用固化图和R〜2值来研究模型的良好性。两套模型在适合的良好方面提供了良好的结果,但它们具有不同的校准因子的值。单向模型的校准系数更接近一个值,表明在可转换性方面具有更好的性能。

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