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Bayesian Approach to Developing Context-Based Crash Modification Factors for Medians on Rural Four-Lane Roadways
Transportation Research Record: Journal of the Transportation Research Board ( IF 1.6 ) Pub Date : 2021-04-26 , DOI: 10.1177/03611981211007141
Xiaobing Li 1 , Jun Liu 2 , Chenxuan Yang 2 , Timothy Barnett 1
Affiliation  

Rural four-lane roadways provide important transportation accessibility and mobility to populations in rural areas. It is a challenge for practitioners to determine cross-section types when both benefits and costs need to be considered. Crash Modification Factors (CMFs) are developed to evaluate the safety effectiveness of alternative designs. However, safety effectiveness could vary significantly across contexts. Thus, this study aims to estimate CMFs for alternative cross sections of rural four-lane roadways under different contexts characterized by traffic volume, truck percentage, and access point density. Using Georgia state-wide crash data, this study developed Safety Performance Functions (SPFs) to predict crash frequencies for different contexts. Considering linearity and independence assumptions of traditional negative binomial SPFs, this study adopts Bayesian generalized negative binomial modeling approaches to relax those assumptions and only follows the Bayes rule to form SPFs for CMF estimation. This study focuses on four typical cross-sections including: (1) non-traversable medians; (2) two-way-left-turn lanes; (3) 4-ft flush medians; and (4) undivided roadways with double-yellow lines (the base cross-section design). The results show that CMFs vary significantly across different contexts. Compared with the base cross-section design, safety benefits of the other three designs can be either positive or negative under different traffic or road conditions. For example, 4-ft flush medians are found to have positive safety benefits (CMF < 1) under lower average daily traffic volumes (e.g., ≤ 6,000) and negative benefits (CMF > 1) under greater average daily traffic volumes (e.g., ≥ 15,000). The findings suggest that, to enhance roadway safety, practitioners should vary cross-section designs for different rural contexts.



中文翻译:

贝叶斯方法为农村四车道道路中位数开发基于上下文的碰撞修正因子

农村的四车道道路为农村地区的人口提供了重要的交通便利性和流动性。当需要同时考虑收益和成本时,确定横截面类型对从业人员是一个挑战。开发了碰撞修正因子(CMF)来评估替代设计的安全性。但是,安全效果在不同情况下可能会有很大差异。因此,本研究旨在估计在以交通量,卡车百分比和接入点密度为特征的不同背景下,农村四车道道路替代横截面的CMF。该研究使用佐治亚州全州的碰撞数据,开发了安全性能功能(SPF),以预测不同情况下的碰撞频率。考虑到传统的负二项式SPF的线性和独立性假设,本研究采用贝叶斯广义负二项式建模方法来放宽这些假设,并且仅遵循贝叶斯规则形成SPF进行CMF估计。这项研究的重点是四个典型的横截面,包括:(1)不可穿越的中位数;(2)左转两条行车线;(3)4英尺齐平中位数;(4)双黄线的无分隔巷道(基础横截面设计)。结果表明,CMF在不同情况下差异很大。与基础横截面设计相比,在不同的交通或道路条件下,其他三种设计的安全性优势可以是正面的,也可以是负面的。例如,在较低的平均每日流量(例如,≤6,000)下,发现4英尺齐平的中值具有正的安全效益(CMF <1)和负的效益(CMF> 1)每日平均流量较高(例如,≥15,000)。研究结果表明,为了提高道路安全性,从业人员应针对不同的农村环境改变横截面设计。

更新日期:2021-04-27
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