Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems

Xiang Li, Qipeng Yan, Yafeng Ma () and Chen Luo
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Xiang Li: School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
Qipeng Yan: School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
Yafeng Ma: School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China
Chen Luo: College of Air Traffic Management, Civil Aviation Flight University of China, Deyang 618307, China

Sustainability, 2023, vol. 15, issue 10, 1-24

Abstract: Public transport, especially bus and metro, are fundamental elements of sustainable transport systems. However, a dearth of research has been devoted to exploring the correlation between the built environment and the intermodal transfer modes that link bus and metro. To address this research gap, this study aims to explore the relationship between the built environment and transfer ridership by examining transfer ridership across different modes. First, this study uses Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) data collected in the city of Chengdu to identify the ridership of Metro-to-Bus (M-B) and Bus-to-Metro (B-M) transfer passengers using dynamic transfer time thresholds. A multi-scale geographically weighted regression model (MGWR) is employed to examine the impact of the built environment on M-B and B-M transfer modes and their scale effects. The findings demonstrate that the MGWR model is effective in capturing the spatial heterogeneity and scale effects of the interrelationships between different built environment factors in the M-B and B-M modes. Furthermore, the impact of different built environment factors on transfer ridership varies. In particular, the number of bus stops and lines have a more pronounced positive effect on promoting transfer ridership, while the density of non-motorway lanes has a significant negative effect. This research provides valuable insights for public transportation management and supports the seamless integration of bus and metro systems to optimize transfer services.

Keywords: metro and bus system; transfer ridership; multiple geographically weighted regression; spatial variations; connectivity of different transportation systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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