Spatio-Temporal Ensemble Method for Car-Hailing Demand Prediction

Abstract

Accurate demand prediction plays a significant role in online car-hailing platforms. With ensemble learning, several models can be combined into a single demand predictive model, achieving low prediction error. Nevertheless, the existing ensemble methods are not intended for spatio-temporal data and thus cannot deal with it. In this article, a spatio-temporal data ensemble model is proposed to predict car-hailing demands. Treating the prediction results as various channels of an image, the proposed ensemble module first compresses and then restores the results using the fully convolutional network. Additionally, a skip connection is used to preserve both the fine-grained information in the shallow layers and the deep coarse information. Based on the principle of model as a service, any model can be plugged into our framework as base models to improve the prediction accuracy. Experimental results demonstrate the effectiveness of the presented model.