主管单位:中国科学技术协会
主办单位:中国地理学会
承办单位:华东师范大学

世界地理研究 ›› 2026, Vol. 35 ›› Issue (4): 153-166.DOI: 10.3969/j.issn.1004-9479.2026.04.20240750

• 城市与产业 • 上一篇    

建成环境对共享单车骑行密度的时空影响分析

李娜(), 王天群()   

  1. 天津城建大学经济与管理学院,天津 300384
  • 收稿日期:2024-09-20 修回日期:2024-12-30 出版日期:2026-04-15 发布日期:2026-04-29
  • 通讯作者: 王天群
  • 作者简介:李娜(1980—),女,副教授,博士,研究方向为城市时空大数据,E-mail:llnn49@tcu.edu.cn
  • 基金资助:
    天津市哲学社会科学规划项目(TJGL23-004)

Analysis of the spatial and temporal impact of the built environment on shared bicycle riding density:Taking Shenzhen as an example

Na LI(), Tianqun WANG()   

  1. School of Economics and Management, Tianjin Chengjian University, Tianjin 300384, China
  • Received:2024-09-20 Revised:2024-12-30 Online:2026-04-15 Published:2026-04-29
  • Contact: Tianqun WANG

摘要:

共享单车作为一种绿色出行的交通方式,不同区域骑行量的时空非平稳性被认为与其建成环境有关。以深圳市共享单车分布较为集聚的区域为研究范围,基于城市多源大数据,从建成环境“5Ds”维度甄选自变量,分别构建地理加权回归模型(GWR)与时空地理加权回归模型(GTWR)并进行对比,从时间和空间两个维度分析建成环境对共享单车骑行密度的影响。研究结果表明:①相较于传统的GWR模型,GTWR模型具有更好的拟合效果;②时间上共享单车骑行分布受早晚通勤影响,空间上符合城市中心出行活动特征并且具有明显的聚类现象;③从建成环境指标来看,建筑密度、路网密度、餐饮类POI密度与交通类POI密度的增加会促进共享单车的使用,尤其在工作日的早晚高峰时段和中心城区位置;POI多样性、距市中心距离与距公交车最近距离则对共享单车的出行具有抑制作用。研究结论能为城市未来制定合理交通策略或可持续城市规划提供更加精确的指导建议。

关键词: 城市交通, 时空异质性分析, 时空地理加权回归模型, 共享单车, 建成环境

Abstract:

As a green mode of transportation, the shared bicycles' spatiotemporal nonstationarity of ridership across different areas is believed to be influenced by the built environment. This study focused on the area where the distribution of shared bicycles is relatively concentrated in Shenzhen, examining the relationship between the built environment and shared bicycle ridership using multisource urban big data. It selected independent variables based on the "5Ds" dimensions of the built environment and compares the performance of the Geographically Weighted Regression (GWR) model with the Geographically and Temporally Weighted Regression (GTWR)model. The analysis considered both temporal and spatial dimensions to assess the impact of the built environment on shared bicycle ridership density. The results indicate that:①the GTWR model provides a better fit than the traditional GWR model. ②Temporally, shared bicycle ridership is influenced by morning and evening commutes, while spatially, it aligns with the characteristics of urban center activities, exhibiting significant clustering. ③In terms of built environment indicators, the increasing of the building density, road network density, catering POI density and traffic POI density significantly promote shared bicycle use, particularly during peak hours on weekdays and in central urban areas. Conversely, the POI diversity, the distance from the city center and the nearest distance to the bus station have a suppressive effect on shared bicycle usage. The results can provide more precise guidance for future rational transportation strategies or sustainable urban planning.

Key words: urban traffic, spatio-temporal heterogeneity, geographically and temporally weighted regression model, shared bicycle, built environment