关键词: 公共自行车, 大数据, 建成环境, 使用率, 南京桥北

Abstract:

In the content of sustainable and smart city development, more and more cites in the world start building Bicycle Sharing System (BSS) which is a green and low-carbon way to travel. Most previous studies on BSS focus on analysis of trip characteristics with small sample survey and simple statistical analysis in China, few studies focus on quantitatively exploring built environment factors that affecting public bicycle flows and usage employing real bicycle usage data. In this article we aim to build a method to examine the influence of the built environment attributes on arrival and departure flows at station level using real bicycle usage data and combined with internet open data. Then by taking Qiaobei area of Nanjing as study object, first we analyzed the temporal and spatial characteristics of public bicycle usage, then we examine the influence of bicycle infrastructure, daily service facilities (such as super market restaurant) and built environment attributes on arrival and departure flows at station level using the a multiple linear regression model. The results show that: 1) There are significant spatial difference between station distribution and usage. Meanwhile there are significant differences in the public bicycles usage at different periods; the use of public bicycles is mainly concentrated in the morning and evening peak stage. It indicates that bicyclists mainly use public bicycle for commuting during morning and evening peak. 2) In built environment factors that in a buffer zone of 300 m around each bike sharing station, the length of secondary road in station catchment and the distance to the closest metro station as well as the number of shopping network, restaurant, around station and the capacity of station were most important factors that influence the bike usage. There is a significant positive correlation between the usage of public bicycle and the density of nearby living facilities, including restaurants, shopping centres and metro stations etc.. The closer station of public bicycle sits from the metro station, the higher frequency of public bicycle usage will occur. Another interesting finding is that there is somewhat competition between bus and public bicycle. 3) In different time periods,there are also significant differences between the built environment factors that influencing bike usage. The developed methodology and findings provide some useful information for urban planners and BSS administrators who design or optimize BSS to improve the demand and turnover of bikes at bike stations with the goal of maximizing usage and availability. At the same time, these finds put forward scientific and reasonable reference for the operation and management of the non-pile-type shared bicycle.

Key words: bicycle sharing system, big data, built environment, usage, Nanjing City