地理研究 ›› 2019, Vol. 38 ›› Issue (12): 2859-2872.doi: 10.11821/dlyj020190081

• 研究论文 • 上一篇    下一篇

广州市主城区共享单车骑行目的地时空特征与影响因素

高枫1, 李少英1(), 吴志峰1, 吕帝江2, 黄冠平1, 刘小平3   

  1. 1. 广州大学地理科学学院,广州 510006
    2. 广东国地规划科技股份有限公司,广州 510650
    3. 中山大学地理科学与规划学院,广州 510275
  • 收稿日期:2019-01-28 修回日期:2019-03-14 出版日期:2019-12-20 发布日期:2019-12-25
  • 通讯作者: 李少英
  • 作者简介:高枫(1996- ),男,广东中山人,硕士,研究方向为地理大数据与城市研究。E-mail: vincenttheone@163.com
  • 基金资助:
    广州市科技计划项目(201904010198);国家自然科学基金项目(41871290);国家自然科学基金项目(41401432);国家自然科学基金项目(41671430);广东省普通高校特色创新类项目(2015KTSCX103)

Spatial-temporal characteristics and the influencing factors of the ride destination of bike sharing in Guangzhou city

GAO Feng1, LI Shaoying1(), WU Zhifeng1, LV Dijiang2, HUANG Guanping1, LIU Xiaoping3   

  1. 1. School of Geographical Science, Guangzhou University, Guangzhou 510006, China
    2. Guangdong Guodi Planning Technology Co., Ltd, Guangzhou 510650, China
    3. School of Geography Science and Planning, Sun Yat-Sen University, Guangzhou 510006, China
  • Received:2019-01-28 Revised:2019-03-14 Online:2019-12-20 Published:2019-12-25
  • Contact: LI Shaoying

摘要:

已有共享单车骑行影响因素研究主要关注起点,大多忽略目的地,在探讨其影响因素的时间差异及交互作用方面较少。以广州市主城区为例,引入地理探测器,精细分析目的地分布影响因素的时间差异,并进行交互探测。研究发现:① 早高峰到达量大于晚高峰,早高峰目的地多分布在CBD,信息产业园和职住平衡地区,晚高峰多分布在地铁3号线体育西至华师站沿线和高密度住宅区。② 服务设施类是影响最显著的类别,其次是交通可达、土地利用和自然环境类别,其中影响力较大的因子依次是住宅、餐饮、公司、购物设施分布、路网密度、距地铁站口距离和POI多样性。③ 各因子影响力存在明显时间差异,所有建成环境因子在早晚高峰时段影响力均大于其他时段,其中公司企业分布因子的影响力在早高峰时段迅速增强。④ 因子间均为双因子增强关系,其中服务设施分布类别中因子交互作用最显著,服务设施分布与交通可达类别的因子交互作用次之。

关键词: 共享单车骑行目的地, 时空特征, 影响因素, 地理探测器, 广州市

Abstract:

Since the emergence of dockless bike sharing in China, it has provided convenience and non-motorized travel mode for residents' short distance trips. Bike sharing plays an important role in improving the accessibility of public transportation and reducing the motorized pollution. At the same time, it also brings out urban issues, such as excessive amount of bike sharing, and mismatch between supply and demand of bike sharing. The main reason for these problems is the lack of accurate prediction and effective scheduling for bike sharing ride. Exploring the spatial and temporal characteristics of bike sharing ride and detecting the influencing factors can provide scientific decision-making basis for precise prediction and effective scheduling of bike sharing. Even though some studies have paid attention to the influencing factors of bike-sharing ride behaviors, most of them focused on the starting point but neglected the destination. Moreover, the temporal difference of influencing factors and the interaction between the factors were seldom revealed in the previous studies. Taking mobike in Guangzhou city as an example, this study aims to analyze the spatial and temporal characteristics of the ride destination of bike sharing. We detect the temporal differences of the influencing factors of bike sharing ride destination, and further explores the interaction between the determinants by using geographical detector. The results show that: (1) The usage of bike sharing in morning-peak time is greater than that in evening-peak time, and the spatial distribution of bike sharing ride destination has obvious temporal differences. The ride destinations of bike sharing at morning peak period are mainly distributed at CBD, zone of information industry and job-housing balance areas. While the ride destinations at evening peak period are mainly distributed along Metro Line 3 from Tiyuxi station to Huashi station as well as high-density residential areas. (2) The element of service facilities has the greatest impacts on the ride destinations of bike sharing, followed by the accessibility, land use and natural environment elements. To be more specifically, the influencing degree of the factors ranks as follows: residential communities distribution, catering facilities distribution, corporate distribution, shopping facilities distribution, road density, distance to metro station entrances and POI diversity. (3) The influence of each factor has remarkable temporal differences, for example, the influence of corporate distribution factor grows rapidly during the morning peak period. (4) The interaction effect of any two factors on the ride destinations of bike sharing is greater than the effect of one single factor. Among them, the interaction effect of factors which belong to service facilities elements are the greatest, followed by the interaction effects between factors of service facilities and accessibility.

Key words: bike sharing ride destination, spatial-temporal pattern, influencing factor, geographical detector, Guangzhou