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Mobile phone GPS data in urban bicycle-sharing: Layout optimization and emissions reduction analysis

Author

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  • Zhang, Haoran
  • Song, Xuan
  • Long, Yin
  • Xia, Tianqi
  • Fang, Kai
  • Zheng, Jianqin
  • Huang, Dou
  • Shibasaki, Ryosuke
  • Liang, Yongtu

Abstract

As a representation of smart and sustainable city development, bicycle-sharing system is one of the hottest topics in the domains of transportation, public health, urban planning, and so on. In this paper, a model is proposed for analyzing the potential reduction in emissions associated with the adoption of a bicycle-sharing system. Methods are proposed for extracting human travel modes from mobile phone GPS trajectories, together with a geometry-based probability model, to support particle swarm optimization. A comparison study is implemented to analyze the model’s computational efficiency. Based on the resulting optimal layout for the network of bicycle docking stations, and considering demand uncertainty, a multi-scenario integer linear programming model is proposed to optimize rebalancing procedures (i.e., moving bicycles between docking stations according to demand), to determine the detailed design-scale information required. Mobile phone GPS trajectories from approximately 3.7 million local mobilities are used to construct a case study for Setagaya Ward, Tokyo. The results show that, compared with the previous methods, the optimal layout solved by the proposed method could reduce emissions by a further 6.4% and 4.4%. With an increase from 30 to 90 bicycle stations, the adoption of bicycle-sharing can reduce CO2 emissions by approximately 3.1–3.8 thousand tonnes. However, emission reduction will maximally decrease by 21.26% after offset by bicycles production and rebalancing-generated emission.

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  • Zhang, Haoran & Song, Xuan & Long, Yin & Xia, Tianqi & Fang, Kai & Zheng, Jianqin & Huang, Dou & Shibasaki, Ryosuke & Liang, Yongtu, 2019. "Mobile phone GPS data in urban bicycle-sharing: Layout optimization and emissions reduction analysis," Applied Energy, Elsevier, vol. 242(C), pages 138-147.
    • Handle: RePEc:eee:appene:v:242:y:2019:i:c:p:138-147
    DOI: 10.1016/j.apenergy.2019.03.119
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    7. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
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