Traffic-Flow Predictor: Predicting Traffic Volume and Patterns

The estimation of traffic volume is an important part of urban transport management, and it is a necessary step for resource allocation and traffic flow optimization. In this study, we evaluate the performance of various different regression models of the relationship between traffic volume and a diverse collection of variables (e.g., temperature, precipitation (both snow and rain), weather, holiday status, and several temporal features such as year, month, day, hour, minute, and second). The paper utilizes a rich dataset of historical traffic volumes, combined with related environmental and temporal data. Missing values are handled and features standardized according to strict preprocessing methods. Linear Regression, Decision Tree Regression, Random Forest Regression, XGB-regression, & Support Vector Machine (SVM) regression are the regression models considered. Performance evaluation through metrics such as mean absolute error, mean squared error, and R-squared to validate the models for generalization and accuracy. Although the Random Forest Regressor and XGB-Regressor both receive fantastic R-squared values 97 and 90 respectively, suggesting mostly great predictive performance, the Decision Tree Regressor has an impressive R-squared score of 1.0, meaning it perfectly spaced the training data.