SCIENTIA GEOGRAPHICA SINICA ›› 2020, Vol. 40 ›› Issue (7): 1082-1091.doi: 10.13249/j.cnki.sgs.2020.07.005

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Travel Characteristics and Influencing Factors of Sharing Bicycles in Central Urban Areas Based on Geographically Weighted Regression: The Case of Guangzhou City

Wei Zongcai1(), Zhen Feng2,*(), Mo Haitong1, Liu Chenyu1, Peng Danli1   

  1. 1. School of Architecture, South China University of Technology, Guangzhou 510641, Guangdong, China
    2. School of Architecture and Urban Planning, Nanjing University/Jiangsu Provincial Engineering Laboratory of Smart City Design Simulation & Visualization, Nanjing 210093, Jiangsu, China
  • Received:2019-07-23 Online:2020-07-10 Published:2020-12-07
  • Contact: Zhen Feng E-mail:weizongcai@scut.edu.cn;zhenfeng@nju.edu.cn
  • Supported by:
    National Natural Science Foundation of China (41801150, 41571146), Guangdong Provincial Philosophy and Social Sciences Project (GD17YGL01).

Abstract:

With the skyrocketing development of mobile information and communication technology and its penetration into everyday life, time, space and distance have been highly compressed. Spatiotemporal constraints of human behavior have been reduced significantly. As a result, the relationship between residents, time and space has been reconstructed, which further reshapes the pattern of urban residents’ behavior. As a new-type travel mode of ‘Internet + sharing’, sharing bicycle provides a more convenient and diversified choice for urban residents’ daily travel. The extant studies mainly focus on the travel characteristics and modes of public bicycle and their influencing factors, while sharing bicycle has been less touched. This article investigates the spatiotemporal distribution characteristics of sharing bicycle travel trajectories in the central urban area of Guangzhou by using one-week Mobike travel data. Then, this study further explores the impacts of functional density factors of built environment on the travel behaviors of sharing bicycles and to what extent, based on geographically weighted regression method. It has been found that the travel behaviors of Mobike show obvious morning and evening peak hours on both weekdays and weekends, and they are not significantly affected by the thunder showers. On weekdays, the new urban area with more employment opportunities, such as Zhujiang New Town and Tianhe Software Park, has shown higher travel density of sharing bicycles, while on the weekends, the travel density in this urban area has significantly reduced. Furthermore, compared with the morning peak, the travel behaviors at the late peak are more concentrated in the core area, while less in the marginal area. The old urban areas with the characteristics of highly functional mix, dense road network and bicycle-friendly become the main travel areas of sharing bicycles. Among them, the density of public transportation station, functional mixing degree and the density of motor vehicle lane show strongly significant influences on the travel behaviors of sharing bicycles. Moreover, improving the quality of the above-mentioned factors can largely promote the travel behaviors of sharing bicycles in most of research areas. This study can provide references for bike-sharing enterprises to improve their operation and management, the government to enhance the slow traffic environment and the quality of citizens’ travel.

Key words: sharing bicycles, travel characteristics, POI data, Guangzhou, geographically weighted regression

CLC Number: 

  • F219