Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments

Zhenbao Wang (), Jiarui Song, Yuchen Zhang, Shihao Li, Jianlin Jia and Chengcheng Song
Additional contact information
Zhenbao Wang: School of Architecture and Art, Hebei University of Engineering, Handan 056038, China
Jiarui Song: School of Architecture and Art, Hebei University of Engineering, Handan 056038, China
Yuchen Zhang: Department of Urban Studies and Planning, The University of Sheffield, Sheffield S10 2TN, UK
Shihao Li: School of Architecture and Art, Hebei University of Engineering, Handan 056038, China
Jianlin Jia: School of Civil Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Chengcheng Song: Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China

Sustainability, 2022, vol. 14, issue 23, 1-21

Abstract: The accuracy of the regression model of ridership of subway stations depends on the scale range of the built environment around the subway stations. Previous studies have not considered the Modifiable Area Unit Problem (MAUP) to establish the regression model of subway station ridership. Taking Beijing as an example, this paper expanded the built environment variables from “5D” category to “7D” category, added indicators such as parking fee standard and population density factor, and proposed a Multi-Scale Geographical Weighted Regression (MGWR) model of outbound ridership of subway stations with standardized variables. The goodness of fit of regression models under 10 spatial scales or built environment around subway stations are compared, and the spatial heterogeneity of built environment factors under the optimal spatial scale of outbound ridership of subway stations during the morning peak on weekdays is discussed. The results show that: (1) the scale range overlapped by 1000 m radius circular buffer zone and Thiessen polygon has the highest explanatory power for the regression model, and is regarded as the optimal scale range of built environment; (2) the density of office facilities, sports and leisure facilities, medical service facilities, building density and floor area ratio (FAR) has a significant impact on the outbound ridership of all subway stations; (3) office facilities, catering facilities, FAR, number of parking lots, and whether subway stations are transfer stations have a positive impact on outbound ridership. The number of medical service facilities, sports and leisure facilities, bus stops and building density have a negative impact on outbound ridership; (4) the two added factors in this study: parking charge standard and population density, as the influencing factors of the built environment, have a significant impact on the outbound ridership of some subway stations; and (5) the different local coefficients of the built environment factors at different stations are discussed, which indicate the spatial heterogeneity on the outbound ridership. The results can provide an important theoretical basis for the prediction and analysis of demand of ridership at subway stations and the integration of the built environment around the stations.

Keywords: urban traffic; built environment; multi-scale geographically weighted regression (MGWR); subway station; optimal scale range (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/23/16314/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/23/16314/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:23:p:16314-:d:995513

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().