Comparative Analysis of Regression Techniques for Weather-Based Route Optimization

Route optimization, a critical component of modern transportation systems, influences environmental sustainability, safety, and efficiency. A weather-based route planning system was exposed to three regression techniques: Random Forest Regression, Decision Tree Regression, and Support Vector Regression. This research compares them. Different evaluation metrics—such as Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, R-squared, and Explained Variance—are applied to measure how well the models make predictions. The analysis uncovers how each model handles varying weather and traffic patterns, showing where they excel and where they fall short. These insights play a key role in shaping improved routing systems that are better equipped to handle environmental challenges in real time.