Data-Article-A Novel Hybrid Cloud Density and Fuzzy Clustering Algorithm for Analyzing Traffic Condition

Traffic flow analysis and management are the most effective ways to improve traffic and mitigate its unfortunate consequences. In the field of traffic engineering, traffic and its various aspects are defined by analyzing variables such as quantity, speed, and density. This article addresses the challenge of appropriately dealing with the uncertainty of traffic variables and converting traffic data into understandable verbal expressions for drivers and urban planners. The study utilizes a clustering approach to analyze traffic variables and determine the traffic condition. A new fuzzy clustering method has been developed to enhance the performance of clustering methods, which is then used to detect abnormal traffic conditions on a route based on the value of traffic variables. The algorithm and proposed method have been evaluated on the traffic dataset of a high-traffic route in Tehran, the capital of Iran. The implementation results demonstrate the traffic condition on the selected route divided into six clusters.

交通流分析与管理是改善交通状况、缓解交通负面影响的最有效路径。在交通工程领域，交通及其各项特征可通过分析流量、速度、密度等变量加以界定。本文旨在解决两大核心研究难题：一是合理处理交通变量的不确定性，二是将交通数据转化为可供驾驶员与城市规划者理解的文字化表述。本研究采用聚类方法对交通变量进行分析以判定交通运行状态，提出一种新型模糊聚类方法以提升聚类算法的整体性能，并基于交通变量值检测路段的异常交通状况。所提算法与方法在伊朗首都德黑兰某高流量路段的交通数据集上开展了性能评估，实验结果表明，所选路段的交通运行状态被划分为6个聚类簇。