Exploring distance decay pattern of public transport-induced agglomeration and its impacts on train ridership attraction

CITIES 2017: Multi Perspectives on Peri-Urban Dynamics Towards Sustainable Development

地球科学;经济学

Nurlaela, S.^1,2 ; Xia, J.^3,4 ; Tuladhar, D.^3,4 ; Lin, T.^3,4 ; Lie, P.^3,4

Urban and Regional Planning Department, Faculty of Architecture Design and Planning, Indonesia^1

Institut Teknologi Sepulung Nopember, Indonesia^2

Department of Spatial Sciences, School of Earth and Planetary Sciences, Indonesia^3

Curtin University of Technologi, WA, Australia^4

关键词: Determinant factors;    Effective density;    Model prediction;    Propensity score matching;    Public transport;    Public transportation;    train ridership;    Treatment group;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/202/1/012051/pdf
DOI  :  10.1088/1755-1315/202/1/012051

来源: IOP

【 摘 要 】

Public transport infrastructure creates the effect of agglomeration through transportation externalities. Effective density is an accessibility based agglomeration that was raised as a positive externality from public transportation investments. The aim of this paper is to understand whether public transport facility would induce agglomeration around stations and furthermore induce train ridership. A methodology was developed to reveal the causality of effective density on ridership and reduce the confounding effects from land use-related determinant factor. This was shown by the propensity score matching that tested if effect of a station being in the treatment group (high effective density stations) on train ridership was influenced by land use characteristics of catchment stations. The causality of effective density on ridership was compared between station groups. Findings showed the effect of treatment group was higher in the matched sample compare to the unmatched sample. This difference may be assigned as the true effect of public transport induced agglomeration which was higher after controlling the land use characteristics of stations. Thus, the inclusion of land use variables in the model prediction may has the effect of rendering the influence of effective density variable lower in the model. These findings could guide station catchment area planning to maximise effective density benefits on train ridership.