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Land use effects on bicycle ridership: a framework for state planning agencies

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  • Cui, Yuchen
  • Mishra, Sabyasachee
  • Welch, Timothy F.

Abstract

State and local agencies increasingly recognize the importance of bicycling activity and as the number of riders has grown over the past several years, the agencies are becoming more aware of the need to provide better bicycle infrastructure. This paper proposes a series of empirical models and applies them to the State of Maryland in the United States, using a spatial lag approach to explore land use, built environment, demographic, socio-economic, and traffic condition connections to bicycle ridership, defined as the number of bicycle trips generated by a given analysis zone per day. A set of models is proposed for three land-use typologies: urban, sub-urban and rural. The data that drives this analysis was obtained from a recently conducted Household Travel Survey (HTS) in the Baltimore–Washington region in Maryland. Results show that some land uses, socio-economic and demographic characteristics, and transit accessibility are positively correlated with bicycle ridership. Other types of land use, transport system characteristics and income level have an inverse relationship with bicycle ridership. The contributing factors to bicycle ridership vary with land-use typology. This proposed approach could be used to evaluate factors relevant to bicycle demand. State and local agencies are advised to build designated bicycle paths according to traffic conditions and increase bicycle-parking capacity in specific establishments.

Suggested Citation

  • Cui, Yuchen & Mishra, Sabyasachee & Welch, Timothy F., 2014. "Land use effects on bicycle ridership: a framework for state planning agencies," Journal of Transport Geography, Elsevier, vol. 41(C), pages 220-228.
    • Handle: RePEc:eee:jotrge:v:41:y:2014:i:c:p:220-228
    DOI: 10.1016/j.jtrangeo.2014.10.004
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    8. Yunwen Chen & Binhui Wang & Jing Huang & Hei Gao & Xianfan Shu, 2024. "Urban Physical Environments Promoting Active Leisure Travel: An Empirical Study Using Crowdsourced GPS Tracks and Geographic Big Data from Multiple Sources," Land, MDPI, vol. 13(5), pages 1-20, April.

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