Geographically Modeling and Understanding Factors Influencing Transit Ridership: An Empirical Study of Shenzhen Metro

Yuxin He,Yang Zhao,Kwok-Leung Tsui

doi:10.3390/app9204217

Yuxin He, Yang Zhao + Show 1 more

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https://doi.org/10.3390/app9204217

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Journal: Applied sciences	Publication Date: Oct 10, 2019
Citations: 16	License type: CC BY 4.0

Affiliation: City University of Hong Kong

Abstract

Ridership analysis at the local level has a pivotal role in sustainable urban construction and transportation planning. In practice, urban rail transit (URT) ridership is affected by complex factors that vary across the urban area. The aim of this study is to model and explore the factors that impact metro station ridership in Shenzhen, China from a local perspective. The direct demand model, which uses ordinary least squares (OLS) estimation, is the most widely used method of ridership modeling. However, OLS estimation assumes parametric stability. This study investigates the use of a direct demand model on the basis of geographically weighted regression (GWR) to model the local relationships between metro station ridership and potential influencing factors. Real-world Shenzhen Metro smart card data are used to test and verify the applicability and performance of the model. The results show that GWR performs better than OLS estimation in terms of both model fitting and spatial interpretation. The GWR model demonstrates a high level of interpretability regarding the spatial distribution and variation of each coefficient, and thus can provide insights for decision-makers into URT ridership and its complex factors from a local perspective.

Highlights

The dramatic increase in urbanization in the last few decades has made urban rail transit (URT) a central pillar of public transport, due to its efficiency and transport capacity
Identifying the dynamic mechanisms of urban transit is critical to both infrastructure planning and transportation operation, and urban indicators such as URT station ridership must be investigated systematically and comprehensively
Establishing whether the candidate variables are spatially autocorrelated is necessary before the geographically weighted regression (GWR) model can be implemented

Summary

Introduction

The dramatic increase in urbanization in the last few decades has made urban rail transit (URT) a central pillar of public transport, due to its efficiency and transport capacity. Identifying the dynamic mechanisms of urban transit is critical to both infrastructure planning and transportation operation, and urban indicators such as URT station ridership must be investigated systematically and comprehensively. URT ridership is a key factor used to determine a station’s occupation in terms of space and the supporting facilities required. URT ridership is known to be affected by the interaction of specific urban elements (such as land use and socio-economics). Understanding the impact of these elements is essential to accurately estimate travel demand and effectively plan and design.

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