English

Peeking the Impact of Points of Interests on Didi

Computers and Society 2018-04-13 v1 Machine Learning Machine Learning

Abstract

Recently, the online car-hailing service, Didi, has emerged as a leader in the sharing economy. Used by passengers and drivers extensive, it becomes increasingly important for the car-hailing service providers to minimize the waiting time of passengers and optimize the vehicle utilization, thus to improve the overall user experience. Therefore, the supply-demand estimation is an indispensable ingredient of an efficient online car-hailing service. To improve the accuracy of the estimation results, we analyze the implicit relationships between the points of Interest (POI) and the supply-demand gap in this paper. The different categories of POIs have positive or negative effects on the estimation, we propose a POI selection scheme and incorporate it into XGBoost [1] to achieve more accurate estimation results. Our experiment demonstrates our method provides more accurate estimation results and more stable estimation results than the existing methods.

Keywords

Cite

@article{arxiv.1804.04176,
  title  = {Peeking the Impact of Points of Interests on Didi},
  author = {Yonghong Tian and Zeyu Li and Zhiwei Xu and Xuying Meng and Bing Zheng},
  journal= {arXiv preprint arXiv:1804.04176},
  year   = {2018}
}