Revealing the multiscale impact of external factors on dockless bike sharing and metro integration: Evidence from Shenzhen

Abstract

Dockless bike sharing (DBS) and metro integration offer innovative solutions for the first-mile and last-mile challenges of urban transportation. However, accurately identifying transfer passenger volume, analyzing DBS and metro integration characteristics, and understanding the spatial heterogeneity of factors affecting this volume remain areas to explore. This study, using Shenzhen as a case, constructs a framework to identify transfer passenger volume by considering built environment elements around metro stations and applying a Thiessen polygon. Transfer passenger volumes are classified into six categories, and their spatio-temporal characteristics are analyzed. A multiscale geographically weighted regression (MGWR) model investigates the impacts of socio-economic attributes, built environment, and transportation-related factors on transfer passenger volume, revealing spatial heterogeneity. Results show that the demand for DBS and metro integration is higher in the morning peak than in the evening peak, with access transfer passenger volume peaks occurring earlier than egress peaks. Spatially, transfer passenger volume shows heterogeneity, with higher demand in the urban core than in the suburbs. Commercial and residential and enterprises have the most significant impact on various types of transfer passenger volume, showing the highest spatial heterogeneity in enterprises. In contrast, science education and culture positively influence evening peak access transfer volume and exhibit the weakest spatial heterogeneity. Population density, sports and leisure, shopping and entertainment, and tourist attractions display localized positive or negative impacts. These findings can guide operators and planners in optimizing public transportation resources to enhance DBS and metro integration.