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Study on Clustering of Free-Floating Bike-Sharing Parking Time Series in Beijing Subway Stations

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  • Dandan Xu

    (Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Yang Bian

    (Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Jian Rong

    (Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Jiachuan Wang

    (Beijing Transportation Information Center, Beijing 100073, China)

  • Baocai Yin

    (Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

Abstract

In recent years, the free-floating bike-sharing (FFBS) system has become a significant mode of travel to satisfy urban residents’ travel demands. However, with the rapid development of FFBS, many problems have arisen, among which the parking problem is the most prominent. To solve the FFBS parking problem around urban subways, firstly, the time series of FFBS parking pattern and subway station classification in Beijing were constructed based on parking intensity, showing a significant spatial distribution of subway stations with different intensity levels. Second, a hierarchical clustering method based on dynamic time warping (DTW) was proposed to cluster the FFBS parking time series. Subway stations in Beijing were grouped into 11 clusters, and the clustering purity reached 0.939, which achieved the expected effect. Then, the peak and off-peak period features of time series were extracted to discuss the clustering results. Finally, a two-level early-warning index for monitoring FFBS was constructed, which took the real-time parking quantity and land use capacity of FFBS into consideration. And FFBS parking management strategies for different early-warning indices were put forward. It is very important for the sustainable development of FFBS and cities.

Suggested Citation

  • Dandan Xu & Yang Bian & Jian Rong & Jiachuan Wang & Baocai Yin, 2019. "Study on Clustering of Free-Floating Bike-Sharing Parking Time Series in Beijing Subway Stations," Sustainability, MDPI, vol. 11(19), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5439-:d:272514
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    References listed on IDEAS

    as
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    Cited by:

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    2. Dandan Xu & Xiaodong Zhang & Xinghua Zhang & Yongguang Yu, 2022. "Type Identification of Land Use in Metro Station Area Based on Spatial–Temporal Features Extraction of Human Activities," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    3. Wendong Chen & Xize Liu & Xuewu Chen & Long Cheng & Jingxu Chen, 2025. "Deciphering flow clusters from large-scale free-floating bike sharing journey data: a two-stage flow clustering method," Transportation, Springer, vol. 52(1), pages 155-184, February.
    4. Romm, Daniel & Verma, Priyanka & Karpinski, Elizabeth & Sanders, Tracy L & McKenzie, Grant, 2022. "Differences in first-mile and last-mile behaviour in candidate multi-modal Boston bike-share micromobility trips," Journal of Transport Geography, Elsevier, vol. 102(C).
    5. Dandan Xu & Yang Bian & Shinan Shu, 2020. "Research on the Psychological Model of Free-floating Bike-Sharing Using Behavior: A Case Study of Beijing," Sustainability, MDPI, vol. 12(7), pages 1-18, April.
    6. Zhan Gao & Sheng Wei & Lei Wang & Sijia Fan, 2020. "Exploring the Spatial-Temporal Characteristics of Traditional Public Bicycle Use in Yancheng, China: A Perspective of Time Series Cluster of Stations," Sustainability, MDPI, vol. 12(16), pages 1-17, August.

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