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.