Optimal Subsampling Methods in Bike Sharing Data Analysis

Abstract

\n Bike sharing system are popular around the world. Traditional bike sharing system require the bikes to be returned to fixed stations, while morden system allows users to leave bikes wherever they like, ready for the next user to pick them up. Smartphone use GPS signal to keep track of its bikes and monitor where most bikes are used and where to place them. Smartphone simultaneously collect many other information such as weather condition, temperature and so on, these features have influence on the delivering amount of bikes. Due to the extensive number of smartphone users, big data technique is requried to handle this situation. We apply subsample method to this smartphone collected big data. In this paper, we derive non-uniform sampling distributions and propose optimal subsampling algorithm. We apply the proposed optimal subsampling algorithm to analyze the smartphone collected bike sharing data set, perfrom extensive computer experiments to evaluate the numerical performance of the proposed sampling algorithm. Our results indicated that the proposed optimal algorithm outperformed the uniform method and have faster running time than using the whole data set.