Recommending Pick-up Points for Taxi-drivers Based on Spatio-temporal Clustering - Mingyue Zhang; Jianxun Liu; Yizhi Liu; Zhenyang Hu; Liang Yi (2012 International Conference on Cloud and Green Computing, 2012)
Using GPS trajectories to recommend pick-up points for taxi driver comes to be a promising approach of increasing profits and decreasing pollutions. In the existing methods, nearly all the GPS data of a city are computed for recommendation. However, it is time-consuming and not accurate enough owing to too much spatio-temporal noise. Therefore, we propose a novel method of recommending pick-up for taxi driver based on spatio-temporal clustering. It is made up of data preprocessing and real-time recommendation. Firstly, we capture the historical pick-up points by analyzing their intervals. These points are clustered at different time and different regions to create candidate pick-up points. Secondly, after ranking the candidate pick-up points around the taxi, the top-5 valuable pick-up points are recommended for taxi drivers. The experimental results, whose data come from Microsoft Research Asia, show that our method can effectively recommend pick-up points for taxi drivers.