The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models

Yang Liu, Yanjie Ji, Zhuangbin Shi and Liangpeng Gao
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Yang Liu: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Dongnandaxue Road 2, Nanjing 211189, China
Yanjie Ji: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Dongnandaxue Road 2, Nanjing 211189, China
Zhuangbin Shi: Intelligent Transportation System Research Center, Southeast University, Dongnandaxue Road 2, Nanjing 211189, China
Liangpeng Gao: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Dongnandaxue Road 2, Nanjing 211189, China

Sustainability, 2018, vol. 10, issue 12, 1-16

Abstract: Long-distance school commuting is a key aspect of students’ choice of car travel. For cities lacking school buses, the metro and car are the main travel modes used by students who have a long travel distance between home and school. Therefore, encouraging students to commute using the metro can effectively reduce household car use caused by long-distance commuting to school. This paper explores metro ridership at the station level for trips to school and return trips to home in Nanjing, China by using smart card data. In particular, a global Poisson regression model and geographically weighted Poisson regression (GWPR) models were used to examine the effects of the built environment on students’ metro ridership. The results indicate that the GWPR models provide superior performance for both trips to school and return trips to home. Spatial variations exist in the relationship between the built environment and students’ metro ridership across metro stations. Built environments around metro stations, including commercial-oriented land use; the density of roads, parking lots, and bus stations; the number of docks at bikeshare stations; and the shortest distance between bike stations and metro stations have different impacts on students’ metro ridership. The results have important implications for proposing relevant policies to guide students who are being driven to school to travel by metro instead.

Keywords: students; metro ridership; school commuting; spatial variations; geographically weighted regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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