Predicting the Riding Volume of Shared Bikes Based on Weather Factors

Purpose/Significance Weather factors such as temperature, rainfall and wind constitute the nonlinear influence on the riding volume of shared bikes, but the traditional multiple linear regression model and the prediction model based on time sequence often have large errors due to their poor expression ability of the nonlinear relationship. Design/Methodology In this paper, the economic factors such as price and the marketing competition situations are supposed relatively stable. A BP neural network model combined with the user tidal riding rule and geographical spatial Point of Interest data to predict the riding volume of shared bikes affected by weather factors. The two prediction models based on per “day” and per “hour” were respectively trained by the real riding data and weather data in Chengdu city, China. Findings/Conclusions The comparative experimental results indicate that the BP neural network model has higher prediction accuracy. The results also show that the riding volume of shared bikes in different POIs at different time is greatly affected by temperature and rainfall. This study provides a scientific approach to estimate the riding volume and also a basis for the precise delivery and collection of bikes for shared bike enterprises.