Data-Driven Urban Mobility - Comprehensive Predictive Modeling for Traffic Congestion

In the rapidly changing landscape of urbanization, this study delves into traffic congestion management within smart cities. Leveraging a diverse dataset from Kaggle, which includes details such as timestamps, weather conditions, road types, and vehicle specifics, the research examines the temporal dynamics of congestion, identifying patterns across various times of the day, week, and year. By applying machine learning models like Linear Regression, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, SVR, K-Neighbors Regressor, and MLP Regressor, the study evaluates their predictive performance, with a focus on distinctions in effectiveness using Mean Squared Error as a metric. The findings highlight the critical role of historical data and weather factors in accurate congestion forecasting, offering valuable insights for the development of smart cities. This research enhances the understanding of urban mobility challenges and provides a practical framework for implementing proactive traffic management strategies in increasingly complex urban environments.