文章编号： 1672-5328（2020）01-0083-12

林堉楠1，徐媛2，杨家文1
(1. 北京大学深圳研究生院城市规划与设计学院，广东深圳518000；2. 杭州市规划和自然资源局余杭分局，浙 江杭州310000)

摘要： 互联网租赁自行车为城市公共交通出行“最后一公里”问题提供了有效解决方案。为深化对 其接驳轨道交通的理解，基于深圳市ofo 共享单车大数据，研究接驳轨道交通的骑行流量与建成环 境因素的关系。在计算各轨道交通车站骑行接驳范围的基础上，通过描述性分析并采用空间回归模 型诊断影响接驳骑行的建成环境因素。结果表明：居住和办公楼板面积、土地利用混合程度、非机 动车道长度、轨道交通车站的地面出入口数量对接驳车站的骑行流量有显著的正向影响；轨道交通 车站与组团中心的距离、车站为城市综合客运枢纽、车站服务范围内的其他车站数量对于接驳骑行 流量有显著的负向影响；现有公共汽车站和公共自行车租赁点建设完善的地区，骑行接驳轨道交通 车站的流量仍然很高。提出未来的城市规划及管理应充分考虑互联网租赁自行车资源的调度分配和 配套建设，以构建更加灵活稳健的公共交通系统。

关键词： 交通规划；互联网租赁自行车；空间回归模型；ofo 共享单车大数据；建成环境

中图分类号： U491.1+2

文献标识码：A

Built Environment on Linking Bicycle to Rail Transit: Case Study Based on ofo Data in Shenzhen

Lin Yunan1, Xu Yuan2, Yang Jiawen1
(1.School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen Guangdong 518000, China; 2.Yuhang Branch of Hangzhou Planning and Natural Resources Bureau, Hangzhou Zhejiang 310000, China)

Abstract： The Internet bike rental has provided an effective service for the “last mile” of urban public transportation. To better understand the linkage of bicycle and rail transit, this paper analyzes the relationship between bicycle flow accessing to rail transit and built environment based on the ofo big data in Shenzhen. By calculating the accessing distance of bicycle to different rail transit stations, the paper identifies the built environment factors that affect bicycle and rail transit connection using descriptive analysis and spatial regression model. The results show that the floor area of residential and office buildings, scope of mixed land use, the length of bicycle lanes, and the number of ground entrances/exits at rail transit stations have a significant positive impact on bicycle flow volume to the connecting stations. The elements such as distance between rail transit stations and grouped activity center, station located at a comprehensive transportation terminal, and the number of other stations within the service area have a significant negative impact on connecting bicycle flow. The bicycle flow to connection stations is still large in the areas where existing bus stops and public bicycle rental sites are well-developed. The paper points out that future urban planning and management should give full consideration to the Internet bike rental service's allocation and supporting facility construction in order to develop a more flexible and sustainable public transportation system.

Keywords： transportation planning; Internet bike rental; spatial regression model; ofo big data; built environment