论文标题
亲和力传播聚类技术的应用以获取宏观，中索和微观水平的交通事故群集
Application of the Affinity Propagation Clustering Technique to obtain traffic accident clusters at macro, meso, and micro levels
论文作者
论文摘要
事故分组是确定容易发事故的位置的关键步骤。在不同的事故分组模式中，聚类方法具有出色的性能，可在空间中发现不同的事故分布。这项工作介绍了基于相似性和空间数据点分布之间的相似性和差异的标准，用于分组交通事故的亲和力传播聚类（APC）方法。 APC从实例之间的相似性矩阵中提供了更现实的事件分布的表示。结果表明，当获得代表性数据样本时，相似性的偏好参数提供了必要的性能来校准模型并根据所需的特征生成簇。此外，该研究表明，偏好参数作为连续参数有助于校准和控制模型的收敛性，从而使发现聚类模式的努力较少，并且对结果的控制更大。
Accident grouping is a crucial step in identifying accident-prone locations. Among the different accident grouping modes, clustering methods present excellent performance for discovering different distributions of accidents in space. This work introduces the Affinity Propagation Clustering (APC) approach for grouping traffic accidents based on criteria of similarity and dissimilarity between distributions of data points in space. The APC provides more realistic representations of the distribution of events from similarity matrices between instances. The results showed that when representative data samples obtain, the preference parameter of similarity provides the necessary performance to calibrate the model and generate clusters according to the desired characteristics. In addition, the study demonstrates that the preference parameter as a continuous parameter facilitates the calibration and control of the model's convergence, allowing the discovery of clustering patterns with less effort and greater control of the results
