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Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data

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  • Yang, Lin
  • Zhang, Fayong
  • Kwan, Mei-Po
  • Wang, Ke
  • Zuo, Zejun
  • Xia, Shaotian
  • Zhang, Zhiyong
  • Zhao, Xinpei

Abstract

As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the “China Optics Valley” in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields ‘better’ configuration in meeting real-world bike riding needs.

Suggested Citation

  • Yang, Lin & Zhang, Fayong & Kwan, Mei-Po & Wang, Ke & Zuo, Zejun & Xia, Shaotian & Zhang, Zhiyong & Zhao, Xinpei, 2020. "Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data," Journal of Transport Geography, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:jotrge:v:88:y:2020:i:c:s0966692319307902
    DOI: 10.1016/j.jtrangeo.2020.102861
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    References listed on IDEAS

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    2. Ji, Shujuan & Wang, Xin & Lyu, Tao & Liu, Xiaojie & Wang, Yuanqing & Heinen, Eva & Sun, Zhenwei, 2022. "Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis," Journal of Transport Geography, Elsevier, vol. 103(C).
    3. Cai Jia & Yanyan Chen & Tingzhao Chen & Yanan Li & Luzhou Lin, 2022. "Evolutionary Game Analysis on Sharing Bicycles and Metro Strategies: Impact of Phasing out Subsidies for Bicycle–Metro Integration Model," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    4. Rafael H. M. Pereira & Pedro R. Andrade & João Pedro Bazzo Vieira, 2023. "Exploring the time geography of public transport networks with the gtfs2gps package," Journal of Geographical Systems, Springer, vol. 25(3), pages 453-466, July.
    5. Muhammad Ali & Muhammad Daud Kamal & Ali Tahir & Salman Atif, 2021. "Fuel Consumption Monitoring through COPERT Model—A Case Study for Urban Sustainability," Sustainability, MDPI, vol. 13(21), pages 1-12, October.
    6. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Li, Jiangtao & Yang, Zhenzhen & Gao, Ziyou, 2022. "Identifying intercity freight trip ends of heavy trucks from GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).

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