Ensemble Learning for Crowd Flows Prediction on Campus

摘要

Campus security is an increasing-attention problem in recent years. Crowd flows prediction on campus is helpful for people monitoring and can avoid potential risks. In this paper, based on distributed visiting data collection on campus, we propose a crowd flows prediction method with ensemble learning. For feature selection, we introduce more information than people visiting data, such as vocation and weather, and evaluate the feature importance as well as their combinations. For prediction model, we use stacking method with Random Forest, Gradient Boosting Tree and XGBoost for a better performance of prediction. Experimental results show that our method obtain high accuracy for crowd flows prediction with low extra cost.