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Models for estimating zone-level bike kilometers traveled using bike network, land use, and road facility variables

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  • Osama, Ahmed
  • Sayed, Tarek
  • Bigazzi, Alexander Y.

Abstract

Despite the increase in studies that link bike trips with various correlates, research gaps remain, including a lack of empirical tools to predict bike kilometers traveled (BKT) using comprehensive measures. The present study evaluates the impacts of network indicators, land use, and road facility on BKT by developing zone-level ridership models. Land use and road facility data were collected for 134 traffic analysis zones (TAZs) in the City of Vancouver, Canada. In addition, graph theory was used to obtain bike network indicators for each TAZ, including measures of connectivity, continuity, linearity, slope, and length of the bike network. A full Bayesian approach, accounting for spatial random effects among the TAZs, was used to develop the models. The results suggested that more connected, dense, flat, continuous, recreational, and off-street bike networks yielded higher BKT. Models that accounted for spatial effects were found to have better fit than those that did not incorporate spatial effects, which implies the importance of considering spatial effects while modeling BKT at the aggregate level. The models provide insights about the factors that influence BKT and information about the spatial variability of the bike travel within a city, which can be useful for planning bike networks.

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  • Osama, Ahmed & Sayed, Tarek & Bigazzi, Alexander Y., 2017. "Models for estimating zone-level bike kilometers traveled using bike network, land use, and road facility variables," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 14-28.
    • Handle: RePEc:eee:transa:v:96:y:2017:i:c:p:14-28
    DOI: 10.1016/j.tra.2016.11.016
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    Cited by:

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    2. Ben Beck & Meghan Winters & Trisalyn Nelson & Chris Pettit & Simone Z Leao & Meead Saberi & Jason Thompson & Sachith Seneviratne & Kerry Nice & Mark Stevenson, 2023. "Developing urban biking typologies: Quantifying the complex interactions of bicycle ridership, bicycle network and built environment characteristics," Environment and Planning B, , vol. 50(1), pages 7-23, January.
    3. Mogens Fosgerau & Miroslawa Lukawska & Mads Paulsen & Thomas Kj{ae}r Rasmussen, 2022. "Bikeability and the induced demand for cycling," Papers 2210.02504, arXiv.org, revised Dec 2022.
    4. Mohamed Bayoumi Kamel & Tarek Sayed, 2021. "The impact of bike network indicators on bike kilometers traveled and bike safety: A network theory approach," Environment and Planning B, , vol. 48(7), pages 2055-2072, September.
    5. Orvin, Muntahith Mehadil & Fatmi, Mahmudur Rahman & Chowdhury, Subeh, 2021. "Taking another look at cycling demand modeling: A comparison between two cities in Canada and New Zealand," Journal of Transport Geography, Elsevier, vol. 97(C).
    6. Li, Haojie & Zhang, Yingheng & Ding, Hongliang & Ren, Gang, 2019. "Effects of dockless bike-sharing systems on the usage of the London Cycle Hire," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 398-411.
    7. Zhao, Jiahui & Fan, Wei & Zhai, Xuehao, 2020. "Identification of land-use characteristics using bicycle sharing data: A deep learning approach," Journal of Transport Geography, Elsevier, vol. 82(C).
    8. Hu, Beibei & Zhong, Zhenfang & Zhang, Yanli & Sun, Yue & Jiang, Li & Dong, Xianlei & Sun, Huijun, 2022. "Understanding the influencing factors of bicycle-sharing demand based on residents’ trips," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).

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