ZEST: A Hybrid Model on Predicting Passenger Demand for Chauffeured Car Service

Abstract

Chauffeured car service based on mobile applications like Uber or Didi suffers from supply-demand disequilibrium, which can be alleviated by proper prediction on the distribution of passenger demand. In this paper, we propose a Zero-Grid Ensemble Spatio Temporal model (ZEST) to predict passenger demand with four predictors: a temporal predictor and a spatial predictor to model the influences of local and spatial factors separately, an ensemble predictor to combine the results of former two predictors comprehensively and a Zero-Grid predictor to predict zero demand areas specifically since any cruising within these areas costs extra waste on energy and time of driver. We demonstrate the performance of ZEST on actual operational data from ride-hailing applications with more than 6 million order records and 500 million GPS points. Experimental results indicate our model outperforms 5 other baseline models by over 10% both in MAE and sMAPE on the three-month datasets.