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

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.

Automated summary

This study explores the spatial and temporal distribution patterns of sharing bicycle travel behaviors in the central urban area of Guangzhou, China, using one week's travel data for Mobike. The study identifies the influences of built environment density factors on sharing bicycle travel behaviors through geographically weighted regression analysis. The findings reveal peak travel times, main travel areas, and factors affecting residents' travel behaviors, providing valuable insights for improving slow traffic environments in rapidly developing MICTs in China.