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A bikeway network design model for urban areas

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  • Jen-Jia Lin
  • Chia-Jung Yu

Abstract

This work develops a bikeway network design model for cycling in urban areas. The objectives of the proposed model are to minimize cyclist risk, maximize cyclist comfort, maximize service coverage for residents, and minimize the impact of the bikeway on existing traffic. The proposed model considers the following constraints: bikeway type, monetary budgets, path continuities, and value ranges of decision variables. Grey numbers are employed to deal with parameter uncertainties, and the proposed model is developed as a multi-objective grey 0–1 programming problem. A case study of the Xinyi District, Taipei City, is conducted. The grey one-stage algorithm is applied to solve this problem. In total, six non-dominated alternatives are generated for the case study. In comparing model alternatives with the existing network, this work confirms that the existing biking network prioritizes cyclist comfort. Scenario analyses indicate that maintaining the lower bound of the length percentage of wooded areas at 20–40 % generates better performance than that of the other scenarios, while decreasing the number of traffic accidents only decreases cyclist risk and does not alter the other objectives. The proposed model is the first network design model for bikeways in literature and will assist bikeway planners in developing alternatives for further evaluation and in developing detailed designs efficiently and systematically. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Jen-Jia Lin & Chia-Jung Yu, 2013. "A bikeway network design model for urban areas," Transportation, Springer, vol. 40(1), pages 45-68, January.
    • Handle: RePEc:kap:transp:v:40:y:2013:i:1:p:45-68
    DOI: 10.1007/s11116-012-9409-6
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    References listed on IDEAS

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    1. Jen-Jia Lin & Cheng-Min Feng, 2003. "A bi-level programming model for the land use – network design problem," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 37(1), pages 93-105, February.
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    1. Wu, Weitiao & Li, Yu, 2024. "Pareto truck fleet sizing for bike relocation with stochastic demand: Risk-averse multi-stage approximate stochastic programming," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    2. Siying Zhu & Feng Zhu, 2020. "Multi-objective bike-way network design problem with space–time accessibility constraint," Transportation, Springer, vol. 47(5), pages 2479-2503, October.
    3. Paulsen, Mads & Rich, Jeppe, 2023. "Societally optimal expansion of bicycle networks," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    4. Garyfallos Arabatzis & Chrysovalantis Malesios & Georgios Kolkos & Apostolos Kantartzis & Panagiotis Lemonakis, 2024. "Quality of Life in the City of Trikala (Greece): Attitudes and Opinions of Residents on Green Spaces and Cycling Paths," Land, MDPI, vol. 13(11), pages 1-18, November.
    5. Ospina, Juan P. & Duque, Juan C. & Botero-Fernández, Verónica & Montoya, Alejandro, 2022. "The maximal covering bicycle network design problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 222-236.
    6. Zuo, Ting & Wei, Heng, 2019. "Bikeway prioritization to increase bicycle network connectivity and bicycle-transit connection: A multi-criteria decision analysis approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 52-71.

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