ABSTRACT
This paper addresses the critical need for efficient traffic forecasting using a dataset encompassing various environmental and temporal factors influencing traffic volume. Leveraging machine learning techniques, we analyze a comprehensive dataset comprising weather conditions (temperature, precipitation, and cloud cover), holiday schedules, and temporal patterns to predict traffic volume accurately. The dataset, spanning diverse conditions and timescales, offers a rich landscape for predictive modeling. Through feature engineering and model refinement, we aim to develop robust predictive algorithms capable of forecasting traffic volume with precision. Our approach involves employing regression-based models, potentially integrating advanced algorithms like Random Forest or Gradient Boosting, Decision Tree, SVR, Linear Regression, Light GBM Regressor and XG Boost Regressor to optimize predictive accuracy. The study's outcomes aim to contribute to traffic management systems by providing accurate predictions, aiding in proactive resource allocation, optimizing routes, and enhancing overall traffic efficiency. dynamic traffic management.
