Determining spatiotemporal impact areas of incidents plays a significant role in incident impact analysis. Although existing empirical methods have proven to be promising, they suffer from the drawbacks that limit their wide applications in automated freeway safety management. This study presents a data‐driven approach to automatically determining the spatiotemporal impact areas of freeway incidents. The spatiotemporal contour plots were first constructed using three representative traffic measures. Next, a nonrecurrent congestion area identification method based on fuzzy clustering was developed. To distinguish possible multiple independent blocks in the nonrecurrent congestion area, a clustering algorithm based on graph theory was adopted. The incident impact areas were then determined by conducting a postprocessing strategy. The incident records and the associated traffic flow data, collected on I‐5 freeway segments in San Diego Region, CA, were used to evaluate the proposed approach. Experimental results show the proposed approach can automatically and properly determine incident impact areas while accounting for the uncertainty resulting from traffic variations.

中文翻译：

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一种基于模糊和图论的聚类确定高速公路事故影响区域的数据驱动方法

确定事件的时空影响区域在事件影响分析中起着重要作用。尽管现有的经验方法已被证明是有前途的，但是它们遭受的缺点是限制了它们在自动化高速公路安全管理中的广泛应用。这项研究提出了一种数据驱动的方法来自动确定高速公路事故的时空影响区域。时空等高线图首先使用三种有代表性的交通量度来构建。接下来，提出了一种基于模糊聚类的非经常性拥堵区域识别方法。为了区分非经常性拥堵区域中可能存在的多个独立区块，采用了基于图论的聚类算法。然后通过执行后处理策略确定事件影响区域。在加利福尼亚州圣地亚哥地区的I-5高速公路路段上收集的事件记录和相关的交通流数据，用于评估所提出的方法。实验结果表明，该方法可以自动，适当地确定事故影响区域，同时解决交通变化带来的不确定性。