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Modeling Bicycle Facility Operation: a Cellular Automaton Approach

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  • Gould, Gregory
  • Karner, Alex

Abstract

State-of-the-art methods for the safe and efficient design of bicycle facilities are based on difficult to collect data and potentially dubious assumptions regarding cyclist behavior. Simulation models could offer a way forward, but existing bicycling models in the academic literature have not been validated using actual data. This paper attempts to address both of these shortcomings simultaneously by conducting a field study to obtain real-world bicycle data and implementing a simulation using a multilane and inhomogeneous cellular automata model to reproduce the observations. The resulting model is found to emulate field conditions while possibly under-predicting bike path capacity. The analysis indicates that the model's potential as for planning could be high given additional work on the underlying model specification and the collection of additional data. Given current concerns surrounding regional air pollution, climate change and urban congestion, this research is timely. If we begin to see more substantial mode shifting to non-motorized modes, this and similar models could become standard tools in the city or regional planner's toolkit. After a discussion of the context in which this research is being conducted, we review the relevant literature on bicycle facility design and bicycle traffic operation, before summarizing the real-world data collection methods and results. The simulation model is then presented along with results and discussion comparing the modeled to observed data. We conclude with suggestions for future work in data collection and model development.

Suggested Citation

  • Gould, Gregory & Karner, Alex, 2009. "Modeling Bicycle Facility Operation: a Cellular Automaton Approach," Institute of Transportation Studies, Working Paper Series qt1xn1j5vh, Institute of Transportation Studies, UC Davis.
  • Handle: RePEc:cdl:itsdav:qt1xn1j5vh
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    References listed on IDEAS

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    Cited by:

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    2. Rui Jiang & Mao-Bin Hu & Qing-Song Wu & Wei-Guo Song, 2017. "Traffic Dynamics of Bicycle Flow: Experiment and Modeling," Transportation Science, INFORMS, vol. 51(3), pages 998-1008, August.
    3. Xue, Shuqi & Jia, Bin & Jiang, Rui & Li, Xingang & Shan, Jingjing, 2017. "An improved Burgers cellular automaton model for bicycle flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 487(C), pages 164-177.
    4. Tang, Tie-Qiao & Rui, Ying-Xu & Zhang, Jian & Wang, Tao, 2018. "Impacts of group behavior on bicycle flow at a signalized intersection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1205-1215.
    5. Jin, Sheng & Qu, Xiaobo & Zhou, Dan & Xu, Cheng & Ma, Dongfang & Wang, Dianhai, 2015. "Estimating cycleway capacity and bicycle equivalent unit for electric bicycles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 225-248.
    6. Paulsen, Mads & Rasmussen, Thomas Kjær & Nielsen, Otto Anker, 2019. "Fast or forced to follow: A speed heterogeneous approach to congested multi-lane bicycle traffic simulation," Transportation Research Part B: Methodological, Elsevier, vol. 127(C), pages 72-98.

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    UCD-ITS-RR-09-10; Engineering;

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