Deep Adaptive Feature Fusion for OriginDestination Passenger Flow Forecasting in Mass Events

The primary objective of this project is to predict passenger inflow and outflow during mass events using a Deep Adaptive Feature Fusion approach. This system is designed to forecast the number of attendees expected at an event and the flow of passengers in and out of the venue. It leverages machine learning algorithms such as Random Forest, Linear Regression, Stacking Regressor, and XGBoost Regression to analyze historical event data, including time, location, and event type