Bicycle Sharing Demand Analysis and Operation Community Division based on Riding Spatial-Temporal Data -Taking Beijing as an Example

Abstract

Under the background of vigorously building sustainable cities, shared bicycles have developed rapidly, and some operation and management problems have gradually become prominent. In recent years, the research based on spatial-temporal big data of shared bicycles mostly focuses on the analysis of hotspots in time and space, and the current management of shared bicycle is mostly divided into operating communities by administrative divisions. The construction of bicycle operation management is not carried out in combination with the characteristics of spatial-temporal data and actual needs. Based on the shared bicycle order data in Beijing, this paper analyzes the basic spatial and temporal characteristics of shared bicycles, and uses geographically weighted regression model to analyze the impact of various types of built environments on the demand distribution for shared bicycles. Finally using Fast Unfolding algorithm to identify communities with close demand for shared bicycles. The study finds that the overall utilization rate of bicycle resources is currently low, and various types of POIs have different ranges and degrees of impact on parking demand, as well as the division of bicycle communities does not fully coincide with administrative divisions. This study helps to understand the overall demand and impact characteristics of shared bicycles, provides a basis for the development of differentiated strategies for the delivery, management, and scheduling of shared bicycles, and helps urban managers to optimize the design of the shared bicycle system.