A Taxi Gap Prediction Method via Double Ensemble Gradient Boosting Decision Tree

Predicting the gap between taxi demand and supply in taxi booking apps is completely new and important but challenging. However, manually mining gap rule for different conditions may become impractical because of massive and sparse taxi data. Existing works unilaterally consider demand or supply, used only few simple features and verified by little data, but not predict the gap value. Meanwhile, none of them dealing with missing values. In this paper, we introduce a Double Ensemble Gradient Boosting Decision Tree Model(DEGBDT) to predict taxi gap. (1) Our approach specifically considers demand and supply to predict the gap between them. (2)them. (2) Also, our method provides a greedy feature ranking and selecting method Also, our method provides a greedy feature ranking and selecting method to exploit most reliable feature. (3) To deal with missing value, our model takes the lead in proposing a double ensemble method, which secondarily integrates different Gradient Boosting Decision Tree(GBDT) model at the different data sparse situation. Experiments on real large-scale dataset demonstrate that our approach can effectively predict the taxi gap than state-of-the-art methods, and shows that double ensemble method is efficacious for sparse data.