Short-Term Demand Forecasting for on-Demand Mobility Service

Abstract

To improve the mymargin efficiency of urban on-demand mobility services (OMS) (e.g., taxi and for-hire vehicles such as Uber, Lyft, and Didi), it is important to frame proactive operation strategies before the actual demand is revealed. The task is challenging since the effectiveness depends on the knowledge of passenger demand distribution in immediate future and is prone to prediction errors. In this study, we develop the boosting Gaussian conditional random field (boosting-GCRF) model to accurately forecast the distribution of short-term future OMS demand using historical OMS demand data. Comprehensive numerical experiments are conducted to evaluate the performance of boosting-GCRF as compared to four other benchmark algorithms. The results suggest that the boosting-GCRF is superior with the best mean absolute percentage error being 14%. In addition, the model is found to be robust under demand anomalies, and the density functions generated by the boosting-GCRF model are found to well capture the actual distribution of the short-term taxi demand.