Leveraging Environmental Data for Intelligent Traffic Forecasting in Smart Cities

This research revolves around the intersection of environmental data and smart city infrastructure to develop an innovative approach for forecasting traffic patterns. In an era of urbanization and the proliferation of smart cities, managing traffic congestion is a critical challenge. This research explores the utilization of air pollution data, a readily available environmental metric, to intelligently predict traffic patterns and improve urban mobility. The study will delve into the potential correlations between air pollution levels and traffic congestion, considering factors such as vehicular emissions, weather conditions, and geographical attributes. By harnessing the power of big data analytics and machine learning techniques, this research aims to develop a predictive model that leverages real-time air pollution data for traffic forecasting. The K-Nearest Neighbors (KNN) model performs better than all other regression models evaluated in this study, according to our findings. The KNN model considerably lowers the error rate in traffic congestion prediction by more than 28%, according to experimental results.