Prediction Model of Demand for Shared Bikes based on Bayesian Theory

Abstract

Some cars sharing bicycle parking lots can be borrowed at will or bicycles pile up. The temporal and spatial influencing factors of shared bicycle historical data in Xiamen are extracted, the region is divided by regular grid, the data extracted from a region are processed and the distribution of the data is fitted, and the fitting accuracy is analyzed. Using Bayesian prediction theory, the demand prediction model of shared bicycles in any time period, different riding time, long distance and distance is obtained, which can be put into use for enterprises, so that enterprises can invest in shared bicycles more scientifically and reasonably.