Design and implementation of analysis and visualization of shared bicycle information

Abstract

While the shared bicycle without piles solves the problem of people's "last mile"travel, its free mobility also brings the embarrassing situation of "one car is idle, the other is looking for it". In this paper, data analysis methods are discussed to reveal the different flow patterns of shared bicycles usage and mobility variation with seasons, weather, time periods, working days, etc; predict method is given for the number of shared bicycle rides based on random forest algorithm. Mastering the distribution and riding rules of shared bicycles can help improve the efficiency of the use of bicycles, better meet the riding needs of users, and provide a data-based basis for regulating the regional regulation of shared bicycles.