Table 2.

Comparison of core contributions against existing works.

Key Contribution	Existing Works	Proposed Work
Outlier Detection and Mitigation	Lacks comprehensive methods for identifying and mitigating outliers, potentially leading to inaccurate traffic forecasting.	Employs empirical analysis and unsupervised learning for robust outlier management, enhancing forecasting accuracy.
Forecast Horizon	Primarily focuses on short-term predictions, with minimal exploration of long-term forecasting challenges.	Includes both short-term and long-term forecasts, examining the impact of forecast horizon on accuracy.
Feature Optimization	Limited investigation into the optimal selection of features for improving model performance.	Conducts experiments with various feature subsets to identify the most effective inputs for forecasting.
Model Selection	Comparisons often span across broad categories, lacking depth within specific model types for traffic forecasting.	Provides a detailed analysis within the boosting model category, offering insights into achieving superior prediction accuracy.