A data-driven approach to determining freeway incident impact areas with fuzzy and graph theory-based clustering
A data-driven approach to determining freeway incident impact areas with fuzzy and graph theory-based clustering
复制标题
一种数据驱动的方法，通过模糊和基于图论的聚类来确定高速公路事故影响区域
DOI：
10.1111/mice.12484
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发表时间：
2020
影响因子：
9.6
通讯作者：
Lu Zhenbo
中科院分区：
文献类型：
--
作者：
Ou Jishun;Xia Jingxin;Wang Yuqing;Wang Chen;Lu Zhenbo
确定事件的时空影响区域在事件影响分析中具有重要作用。尽管现有的经验方法已被证明是有前途的，但它们仍然存在缺陷，限制了它们在高速公路自动安全管理中的广泛应用。本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。首先利用三种代表性交通测度构建时空等高线图。其次，提出了一种基于模糊聚类的非经常性拥塞区域识别方法。为了区分非经常性拥塞区域中可能存在的多个独立块，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。在加州圣地亚哥地区I - 5高速公路路段收集的事故记录和相关交通流量数据用于评估所提出的方法。实验结果表明，该方法能够在考虑交通变化带来的不确定性的情况下，自动准确地确定事故影响区域。
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.