Data Analytics on CO2 Emission using Machine Learning for Urban Transportation Networks

The carbon dioxide (CO2) emissions from urban transportation contribute significantly to global greenhouse gas emissions, accounting for nearly 25% of the total energy-related emissions globally, with urban areas responsible for 60% of transportation-related emissions. Existing urban transportation networks face challenges in accurately modeling CO2 emissions due to data heterogeneity, complex traffic patterns, and insufficient feature extraction techniques. This study proposes a novel data analytics framework integrating advanced Machine Learning methodologies for precise CO2 emission prediction. The proposed methodology begins with comprehensive data preprocessing to handle missing values, normalize datasets, and balance the data distribution. Principal Component Analysis (PCA) is employed for feature extraction, reducing dimensionality while retaining critical information. A Random Forest Regressor (RFR) is utilized for predictive modeling, leveraging its ability to capture nonlinear relationships and its robustness to overfitting. The methodology is validated on the CO2 Emissions dataset, demonstrating improved predictive accuracy and efficiency compared to traditional approaches. By optimizing feature selection and modeling, the framework provides actionable insights for urban planners and policymakers to design sustainable transportation systems.