Mitigating Urban Congestion: A K-Means-Based Pattern Recognition Approach for Quantifiable Mobility and Sustainability Gains

The ever-present issue of urban congestion which is caused by the constant urbanization requires complex interventions using Intelligent Transportation Systems (ITS).ITS do furnishes the basic infrastructure, such as the strong sensing, communication, and control facilities, that are needed in controlling complicated traffic systems.Such systems have to be appropriately implemented to minimize the delays, improve the efficiency of the public transportation, and eventually promote the safety.Conventional traffic management systems are usually based upon aggregate history and fixed time schedules and thus are not adaptive to time variations.The intersection of huge new sources of data and the blistering development of machine learning algorithms allow transportation scientists to consider and predict dynamic traffic conditions more quickly and accurately.The use of Artificial Intelligence (AI) and data analytics, specifically, clustering is the key to achieving sustainable urban mobility, as was proved by the studies that show better transportation efficiency and less adverse effects on the environment in large cities.Traffic control and guidance systems rely heavily on the capability of an ITS to model and forecast traffic flow situations and provide the opportunity to decrease traffic congestion and increase road capacity resourcefully.Information-oriented solutions go beyond the generalized planning, offering specific assistance to the important domains in urban traffic planning, active signal control, and the optimization of emergency response.