The Nonlinear Effect of the Built Environment on Bike–Metro Transfer in Different Times and Transfer Flows Considering Spatial Dependence

Yuan Zhang (), Yining Meng, Xiao-Jian Chen, Huiming Liu and Yongxi Gong
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Yuan Zhang: School of Architecture, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
Yining Meng: School of Architecture, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
Xiao-Jian Chen: Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Huiming Liu: Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China
Yongxi Gong: School of Architecture, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China

Sustainability, 2025, vol. 17, issue 1, 1-25

Abstract: Dockless bike-sharing (DBS) plays a crucial role in solving the “last-mile” problem for metro trips. However, bike–metro transfer usage varies by time and transfer flows. This study explores the nonlinear relationship between the built environment and bike–metro transfer in Shenzhen, considering different times and transfer flows while incorporating spatial dependence to improve model accuracy. We integrated smart card records and DBS data to identify transfer trips and categorized them into four types: morning access, morning egress, evening access, and evening egress. Using random forest and gradient boosting decision tree models, we found that (1) introducing spatial lag terms significantly improved model accuracy, indicating the importance of spatial dependence in bike–metro transfer; (2) the built environment’s impact on bike–metro transfer exhibited distinct nonlinear patterns, particularly for bus stop density, house prices, commercial points of interest (POI), and cultural POI, varying by time and transfer flow; (3) SHAP value analysis further revealed the influence of urban spatial structure on bike–metro transfer, with residential and employment areas displaying different transfer patterns by time and transfer flow. Our findings underscore the importance of considering both built environment factors and spatial dependence in urban transportation planning to achieve sustainable and efficient transportation systems.

Keywords: dockless bike-sharing; built environment; spatial dependence; machine learning; metro transfer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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