The terms “hotspots,” “blackspot,” or “high-risk” are normally used for an area on a road where the number of car accidents are far beyond the normal level and, more importantly, the causes of the incident always link to one or many specific reasons. Identifying these spots is helpful and may assist people in authority to implement corrective actions. To do the aforementioned, some decision-support tools could be useful since they enable us to monitor the indicators that may lead to a car accident, same as the procedure engineers use to control production lines in factories. In this regard, control charts are utilized to identify blackspots. Although the method is low cost, and provides quick detection and statistical reliability level and, more importantly, uses online applications; however, it advised that the rate quality control method could be used on roads with the same geometry and traffic conditions. This research develops a hybrid method, based on accident type, for improving rate quality control method to overcome the shortcomings. Furthermore, the suggested framework considers crash severity and traffic volume that have not been taken into account in the rate quality control method yet. The case study evaluates how this framework is successful to identify blackspots, and based on the result in the real environment, it reveals that the proposed method could detect and identify the hotspots.

中文翻译：

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基于P和P′控制图的混合热点识别方法

“热点”、“黑点”、“高危”等术语通常用于道路上车祸数量远远超出正常水平的区域，更重要的是，事故原因始终与一个或多个特定原因。识别这些点是有帮助的，并且可以帮助权威人士实施纠正措施。为此，一些决策支持工具可能很有用，因为它们使我们能够监控可能导致车祸的指标，就像工程师用来控制工厂生产线的程序一样。在这方面，使用控制图来识别黑点。虽然该方法成本低，但提供快速检测和统计可靠性水平，更重要的是使用在线应用程序；然而，它建议可以在具有相同几何形状和交通条件的道路上使用速率质量控制方法。本研究开发了一种基于事故类型的混合方法，用于改进率质量控制方法以克服这些缺点。此外，建议的框架考虑了速率质量控制方法中尚未考虑的碰撞严重程度和交通量。案例研究评估了该框架如何成功识别黑点，并基于真实环境中的结果，表明所提出的方法可以检测和识别热点。建议的框架考虑了速率质量控制方法中尚未考虑的碰撞严重程度和交通量。案例研究评估了该框架如何成功识别黑点，并基于真实环境中的结果，表明所提出的方法可以检测和识别热点。建议的框架考虑了速率质量控制方法中尚未考虑的碰撞严重程度和交通量。案例研究评估了该框架如何成功识别黑点，并基于真实环境中的结果，表明所提出的方法可以检测和识别热点。