Real-Time Traffic Congestion Prediction Using Predictive Data Analysis

Urban traffic congestion is a growing concern worldwide, affecting travel time, fuel consumption, and environmental pollution. Optimal prediction and control of traffic density have become relevant issues for the city's navigation and maintenance of concrete infrastructures. The paper looks at how it is possible to predict congestion in real-time traffic data of the urban traffic systems. The methodology involves processing data from the network of sensors in re-al-time and past traffic data to create a predictive model that traf-fic-managing systems can use. A comparison of two models is then made, the time–series predictive model and a hybrid regression model in their accuracy of estimation speed of computation, and responsiveness in real-time. Applying sophisticated mathematical formulas for the simulation of traffic flow and congestion this study can be used to show how the integration of technologies and mathematics can improve traffic situation and decrease congestion in cities. These findings are expressed in comparative plots and quantitative measures where all the predictions from the hybrid model compare favourably to the basic time-series model, despite having fewer data points. The proposed implementation relies on real-life traffic data, and the effectiveness of the pre-dictive models is assessed by comprehensive error measures such as mean absolute error (MAE) or root mean square error (RMSE). Consequently, the findings of this study advance the design of intelligent traffic management systems for reducing traffic density to enhance the quality of life in urban areas