Abstract
Traffic congestion is a significant problem affecting the sustainable development of urban traffic. It is important to analyze the congestion and forecast future traffic models to prevent traffic congestion. This study is conducted with the main aim to determine the most congested area of the road network and determine how they are related to the demand of the drivers. This study uses the Floating Car Data method to find the traffic congestion and the degree to which observed congestion clusters are a meaningful representation of congestion patterns within a more extensive urban road network. Statistical calculations have been carried out to determine the correlation between clusters based on which conclusions are drawn. Findings have shown that this approach can effectively identify the traffic congestion patterns in the urban road network. The analyses of the traffic congestion behaviour have shown that congestion is more severe and widespread in evening rush hours than morning. Overall, the results can be used to develop a framework to describe potential traffic issues and a system for predicting congestion.