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

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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)

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.

Suggested Citation

  • Yang Liu & Yanjie Ji & Zhuangbin Shi & Liangpeng Gao, 2018. "The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4684-:d:189180
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    References listed on IDEAS

    as
    1. Kuby, Michael & Barranda, Anthony & Upchurch, Christopher, 2004. "Factors influencing light-rail station boardings in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(3), pages 223-247, March.
    2. Liu, Yang & Ji, Yanjie & Shi, Zhuangbin & He, Baohong & Liu, Qiyang, 2018. "Investigating the effect of the spatial relationship between home, workplace and school on parental chauffeurs’ daily travel mode choice," Transport Policy, Elsevier, vol. 69(C), pages 78-87.
    3. Zhang, Rui & Yao, Enjian & Liu, Zhili, 2017. "School travel mode choice in Beijing, China," Journal of Transport Geography, Elsevier, vol. 62(C), pages 98-110.
    4. Noreen McDonald, 2008. "Children’s mode choice for the school trip: the role of distance and school location in walking to school," Transportation, Springer, vol. 35(1), pages 23-35, January.
    5. Raktim Mitra, 2013. "Independent Mobility and Mode Choice for School Transportation: A Review and Framework for Future Research," Transport Reviews, Taylor & Francis Journals, vol. 33(1), pages 21-43, January.
    6. Chiou, Yu-Chiun & Jou, Rong-Chang & Yang, Cheng-Han, 2015. "Factors affecting public transportation usage rate: Geographically weighted regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 161-177.
    7. Yanjie Ji & Xinwei Ma & Mingyuan Yang & Yuchuan Jin & Liangpeng Gao, 2018. "Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach," Sustainability, MDPI, vol. 10(5), pages 1-23, May.
    8. Singh, Nishant & Vasudevan, Vinod, 2018. "Understanding school trip mode choice – The case of Kanpur (India)," Journal of Transport Geography, Elsevier, vol. 66(C), pages 283-290.
    9. Mitra, Raktim & Buliung, Ron N., 2014. "The influence of neighborhood environment and household travel interactions on school travel behavior: an exploration using geographically-weighted models," Journal of Transport Geography, Elsevier, vol. 36(C), pages 69-78.
    10. Daniel P. McMillen, 2004. "Geographically Weighted Regression: The Analysis of Spatially Varying Relationships," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 554-556.
    11. Xia, Jianhong(Cecilia) & Nesbitt, Joshua & Daley, Rebekah & Najnin, Arfanara & Litman, Todd & Tiwari, Surya Prasad, 2016. "A multi-dimensional view of transport-related social exclusion: A comparative study of Greater Perth and Sydney," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 205-221.
    12. Ermagun, Alireza & Samimi, Amir, 2015. "Promoting active transportation modes in school trips," Transport Policy, Elsevier, vol. 37(C), pages 203-211.
    13. Li, Shengxiao & Zhao, Pengjun, 2015. "The determinants of commuting mode choice among school children in Beijing," Journal of Transport Geography, Elsevier, vol. 46(C), pages 112-121.
    14. Huang, Yuan & Wang, Xiaoguang & Patton, David, 2018. "Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach," Journal of Transport Geography, Elsevier, vol. 69(C), pages 221-233.
    15. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    16. Timothy Ross & Ronald Buliung, 2018. "A systematic review of disability’s treatment in the active school travel and children’s independent mobility literatures," Transport Reviews, Taylor & Francis Journals, vol. 38(3), pages 349-371, May.
    17. McDonald, Noreen C., 2008. "Household interactions and children’s school travel: the effect of parental work patterns on walking and biking to school," Journal of Transport Geography, Elsevier, vol. 16(5), pages 324-331.
    18. Broberg, Anna & Sarjala, Satu, 2015. "School travel mode choice and the characteristics of the urban built environment: The case of Helsinki, Finland," Transport Policy, Elsevier, vol. 37(C), pages 1-10.
    19. Feuillet, T. & Commenges, H. & Menai, M. & Salze, P. & Perchoux, C. & Reuillon, R. & Kesse-Guyot, E. & Enaux, C. & Nazare, J.-A. & Hercberg, S. & Simon, C. & Charreire, H. & Oppert, J.M., 2018. "A massive geographically weighted regression model of walking-environment relationships," Journal of Transport Geography, Elsevier, vol. 68(C), pages 118-129.
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