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Multi-level urban form and commuting mode share in rail station areas across the United States; a seemingly unrelated regression approach

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  • Nasri, Arefeh
  • Zhang, Lei

Abstract

Transit-friendly development has recently become a popular strategy to increase transit mode share in the United States. Many policies and programs have been proposed and implemented across the country to increase residential and job densities, walkability, and mixed-use development around major transit stations in order to encourage transit ridership. Using data from all rail transit stations across the United States, this paper presents an analysis of commute mode share for people who live within walking distance to rail transit with regard to the urban form at both neighborhood and regional levels. This study provides additional evidence to better understand how transit accessibility and urban design jointly aim to encourage transit use and reduce driving in rail stations areas across the U.S. and thus cope with ever-growing traffic congestion in urban areas. It is one of the first analyses to examine the relationships between urban form measured at both local/neighborhood and metropolitan levels and the commuting mode share across major transit station areas nationwide. It utilizes a Seemingly Unrelated Regression modeling method (SUR) -which is based on generalized least squares (GLS)- and estimates three primary modes of auto, transit, and walk/bike.

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  • Nasri, Arefeh & Zhang, Lei, 2019. "Multi-level urban form and commuting mode share in rail station areas across the United States; a seemingly unrelated regression approach," Transport Policy, Elsevier, vol. 81(C), pages 311-319.
    • Handle: RePEc:eee:trapol:v:81:y:2019:i:c:p:311-319
    DOI: 10.1016/j.tranpol.2018.05.011
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    3. Chaoying Yin & Xiaoquan Wang & Chunfu Shao & Jianxiao Ma, 2022. "Exploring the Relationship between Built Environment and Commuting Mode Choice: Longitudinal Evidence from China," IJERPH, MDPI, vol. 19(21), pages 1-15, October.
    4. Su, Shiliang & Wang, Zhuolun & Li, Bozhao & Kang, Mengjun, 2022. "Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications," Journal of Transport Geography, Elsevier, vol. 104(C).
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    6. Tiong, Kah Yong & Ma, Zhenliang & Palmqvist, Carl-William, 2023. "Analyzing factors contributing to real-time train arrival delays using seemingly unrelated regression models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    7. Aston, Laura & Currie, Graham & Kamruzzaman, Md. & Delbosc, Alexa & Fournier, Nicholas & Teller, David, 2020. "Addressing transit mode location bias in built environment-transit mode use research," Journal of Transport Geography, Elsevier, vol. 87(C).

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