Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China

Shaoying Li, Dijiang Lyu, Guanping Huang, Xiaohu Zhang, Feng Gao, Yuting Chen and Xiaoping Liu

Journal of Transport Geography, 2020, vol. 82, issue C

Abstract: Understanding the relationship between the rail transit ridership and the built environment is crucial to promoting transit-oriented development and sustainable urban growth. Geographically weighted regression (GWR) models have previously been employed to reveal the spatial differences in such relationships at the station level. However, few studies characterized the built environment at a fine scale and associated them with rail transit usage. Moreover, none of the existing studies attempted to categorize the stations for policy-making considering varying impacts of the built environment. In this study, taking Guangzhou as an example, we integrated multi-source spatial big data, such as high spatial resolution remote sensing images, points of interest (POIs), social media and building footprint data to precisely quantify the characteristics of the built environment. This was combined with a GWR model to understand how the impacts of the fine-scale built environment factors on the rail transit ridership vary across the study region. The k-means clustering method was employed to identify distinct station groups based on the coefficients of the GWR model at the local stations. Policy zoning was proposed based on the results and differentiated planning guidance was suggested for different zones. These recommendations are expected to help increase rail transit usage, inform rail transit planning (to relieve the traffic burden on currently crowed lines), and re-allocate industrial and living facilities to reduce the commute for the residents. The policy and planning implications are crucial for the coordinated development of the rail transit system and land use.

Keywords: Transit ridership; Built environment; Geographically weighted regression; K-means; Guangzhou (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (33)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:82:y:2020:i:c:s0966692319306192

DOI: 10.1016/j.jtrangeo.2019.102631

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