Discovering traffic anomaly propagation enables a thorough understanding of traffic anomalies and dynamics. Existing methods, such as Outlier-Tree, are not accurate to find out the trend of abnormal traffic for two reasons. First, they discover the propagation pattern based on the detected traffic anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and temporal outlier neighborhoods rather than considering from a global perspective, and thus cannot form a complete propagation tree if a spatial or temporal gap exists. In this paper, we propose a novel discovering traffic anomaly propagation method using the mesh data and enhanced traffic change peaks (en-TCP) to visualize the change of traffic anomalies (e.g., an area where vehicles are gathering or evacuating) and thus accurately capture traffic anomaly propagation. Inspired by image processing techniques, the GPS trajectory dataset in each time bin can be converted to one grid traffic image and be stored in the grid density matrix, in which the grid cell corresponds to the pixel and the density of grid cells corresponds to the Gray level (0∼255) of pixels. An enhanced adaptive filter is developed to generate traffic change graph sequences from grid traffic images in consecutive periods, and clustering en-TCP in a continuous period is to discover the propagation of traffic anomalies. The accuracy and effectiveness of the proposed method have been demonstrated using a real-world GPS trajectory dataset.

中文翻译：

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使用增强的交通变化峰值发现城市空间中的交通异常传播

发现流量异常传播可以全面了解流量异常和动态。现有的方法，如 Outlier-Tree，由于两个原因不能准确地找出异常流量的趋势。首先，他们根据检测到的流量异常发现传播模式。检测方法本身的不完善可能会引入假异常而漏掉真正的异常。其次，他们通过搜索连续的时空异常邻域而不是从全局角度考虑来开发异常传播树，因此如果存在空间或时间差距，则无法形成完整的传播树。在本文中，我们提出了一种新的发现交通异常传播方法，使用网格数据和增强的交通变化峰值（en-TCP）来可视化交通异常的变化（例如，车辆聚集或疏散的区域），从而准确地捕获交通异常传播。受图像处理技术的启发，可以将每个时间 bin 中的 GPS 轨迹数据集转换为一张网格交通图像，并存储在网格密度矩阵中，其中网格单元对应像素，网格单元的密度对应灰度等级 （0～255) 的像素。开发了一种增强的自适应滤波器，从连续周期的网格流量图像中生成流量变化图序列，并在连续周期内对 en-TCP 进行聚类，以发现流量异常的传播。使用真实世界的 GPS 轨迹数据集证明了所提出方法的准确性和有效性。