Chinese Geographical Science ›› 2021, Vol. 31 ›› Issue (1): 54-69.doi: 10.1007/s11769-020-1159-3

• Big Data and Urban Study • Previous Articles     Next Articles

Travel Behaviours of Sharing Bicycles in the Central Urban Area Based on Geographically Weighted Regression: The Case of Guangzhou, China

WEI Zongcai1, ZHEN Feng2, MO Haitong1, WEI Shuqing1, PENG Danli1, ZHANG Yuling3   

  1. 1. School of Architecture, State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510641, China;
    2. School of Architecture and Urban Planning, Nanjing University, Jiangsu Provincial Engineering Laboratory of Smart City Design Simulation&Visualization, Nanjing 210093, China;
    3. Guangzhou Institute of Geography, Guangzhou 510070, China
  • Received:2020-02-10 Published:2020-08-27
  • Contact: ZHEN Feng
  • Supported by:
    Under the auspices of National Natural Science Foundation of China (No. 41801150, 41571146, 41801144), Natural Science Foundation of Guangdong Province (No. 2018A030310392), Guangdong Planning Project of Philosophy and Social Science (No. GD17YGL01), Science and Technology Program of Guangzhou (No. 201906010033), GDAS’ (Guangdong Academy of Sciences) Project of Science and Technology Development (No. 2020GDASYL-20200104007)

Abstract: Mobile information and communication technologies (MICTs) have fully penetrated everyday life in smart societies; this has greatly compressed time, space, and distance, and consequently, reshaped residents’ travel behaviour patterns. As a new mode of shared mobility, the sharing bicycle offers a variety of options for the daily travel of urban residents. Extant studies have mainly examined the travel characteristics and influencing factors of public bicycles with piles, while the travel patterns for sharing bicycles and their driving mechanisms have been largely ignored. Using one week’s travel data for Mobike, this study investigated the spatial and temporal distribution patterns of sharing bicycle travel behaviours in the central urban area of Guangzhou, China; furthermore, it identified the influences of built environment density factors on sharing bicycle travel behaviours based on the geographically weighted regression method. Obvious morning and evening peaks were observed in the sharing bicycle travel patterns for both weekdays and weekends. The old urban area, which had a high degree of mixed function, dense road networks, and cycling-friendly built environments, was the main travel area that attracted sharing bicycles on both weekdays and weekends. Furthermore, factors including the point of interest (POI) for the density of public transport stations, the functional mixing degree, and the density of residential POIs significantly affected residents’ travel behaviours. These findings could enrich discourse regarding shared mobility with a Chinese case characterised by rapidly developing MICTs and also provide references to local authorities for improving slow traffic environments.

Key words: sharing bicycles, travel behaviours, smart societies, geographically weighted regression analysis, Guangzhou, China