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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method
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Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

机译：贝叶斯网络方法评估农村路边安全风险

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Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts’ judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015–2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings.
机译：评估农村路边的安全风险对于合理分配有限的预算并避免过度安装安全设施至关重要。为了评估当事故数据不可用或丢失时农村路边的安全风险，本研究提出了一种贝叶斯网络（BN）方法，该方法使用专家对不同安全风险因素的条件概率的判断来评估农村路边的安全风险。 。考虑了八个因素，其中包括文献中确定的七个因素和一个称为接入点密度的新因素。为了验证所提方法的有效性，在中国南通农村地区使用19.42 km长的道路网络进行了案例研究。通过比较所提出的方法的结果和研究区域2015-2016年的越野（ROR）碰撞数据，发现所提出的方法所识别出的具有较高安全风险等级的路段与较高的路段具有统计学显着的相关性。基于崩溃数据的崩溃严重性。此外，通过比较分别由八个因素和七个因素（已删除的新因素）评估的结果，我们还发现接入点密度显着影响了农村路边的安全风险。这些结果表明，该方法可被认为是一种以相对较高的准确度评估农村路边安全风险的低成本解决方案，尤其是对于乡村大路网和由于预算限制，人为错误，ROR崩溃数据不完整的地区而言。过失或不一致的崩溃记录。

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