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Associations of land use around rail transit stations with jobs–housing distribution of rail commuters from smart-card data.

Authors :
Zhan, Qingming
Jia, Yuqiu
Zheng, Zhenhua
Zhang, Qi
Luo, Lei
Source :
Geo-Spatial Information Science; Sep2023, Vol. 26 Issue 3, p346-361, 16p
Publication Year :
2023

Abstract

Previous studies generally used land use and travel flow to investigate the efficiency of the railway system in Transit-Oriented Development (TOD) cities. Furthermore, to study the association of land use and jobs–housing distribution of commuters, we can find out the potential development of rail commuting. In this research, four railway lines in Wuhan, China, were selected to explore the land use in promoting practical commuting population according to the smart-card data obtained. For land use issues, except the road density and building density, a Normalized Location-Weighted Landscape Index (NLWLI) based on the source – sink theory in landscape ecology was established to assess the jobs–housing land use around rail transit stations. Meanwhile, employment and housing details of commuters around rail transit stations were identified using smart-card data. We found that the generation of commuting flow was affected by building density and the land use of employment in the immediate vicinity of rail transit stations. The distribution of building density and commuters in a mature rail line was roughly a normal distribution. However, due to the inconsistency of land use and jobs – housing distribution around stations, the requirement of the balance of land use and jobs – housing distribution should be reduced at the scale of rail stations in the TOD planning systems. This study is the application of massive smart-card data in the field of urban research. It identifies land use issues that affect rail transit commuting flow, and can help urban planners improve the efficiency of rail transit through planning and design. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10095020
Volume :
26
Issue :
3
Database :
Complementary Index
Journal :
Geo-Spatial Information Science
Publication Type :
Academic Journal
Accession number :
173274906
Full Text :
https://doi.org/10.1080/10095020.2022.2100286