Ensemble Models of For-Hire Vehicle Trips

Abstract

Ensemble forecasting is class of modeling approaches that combines different data sources, models of different types, with different assumptions, and/or pattern recognition methods. By comprehensively pooling information from multiple sources, analyzed with different techniques, ensemble models can be more accurate, and can better account for different sources of real-world uncertainties. The share of for-hire vehicle (FHV) trips increased rapidly in recent years. This paper applies ensemble models to predicting for-hire vehicle (FHV) trips in Chicago and New York City, showing that properly applied ensemble models can improve forecast accuracy beyond the best single model.