Supply-demand prediction of DIDI based on points of interests selection in extreme gradient boosting algorithm

Abstract

In recent years, DiDi, an online car-hailing (OCH) service provider, has emerged as a leader in the sharing economy. To improve user experience, the company must minimize the waiting time and optimize car utilization based on accurate estimation of supply-demand gap. This paper aims to develop a desirable model to select the most significant factors for OCH supply-demand estimation. Firstly, the correlation between the points of interest (POIs) and the supply-demand gap was proved through statistical analysis. Next, the number and type of POIs were found to have a slight impact on the estimation results. On this basis, the authors put forward a method called POI principal component extraction based on supply-demand gap (PPCE-SDG) to select the most significant POIs. The PPCE-SDG involves four steps: k-means clustering (KMC) of blocks based on supply-demand gap; creating a data vector of POIs after counting the POIs in each cluster; extracting the significant POIs through principal component analysis (PCA) of the data vector; importing the extracted POIs to extreme gradient boosting (XGBoost) for OCH supply-demand prediction. Finally, the POIs selected by the PPCE-SDG were proved superior than those collected by other methods in OCH supply-demand estimation, indicating that our model is a desirable tool for significant POIs selection. The research results lay a good basis for the optimization of OCH services.