Abstract
With the rapid growth of bike-sharing comes the challenge of unregulated bike-sharing parking in cities, which can lead to an unbalanced distribution of bikes and negatively impact the user experience and the operating costs of bike-sharing companies. To address these challenges, bike-sharing companies can create temporary parking stations or electronic fencing and implement bicycle rebalancing strategies across districts. However, these strategies require real-time data analysis and should take into account other factors, such as the inflow and outflow of bikes in each zone. To solve this problem, we proposes a composite clustering algorithm based on density and inflow-outflow balance to divide the city into a grid and extract hotspots as suitable areas for bicycle docking stations. Comparative experiments on common clustering algorithms for shared bicycles demonstrate the reasonableness and high precision of our method.
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Guo, L., Li, D., Cai, Z. (2023). A Composite Grid Clustering Algorithm Based on Density and Balance Degree. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_4
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DOI: https://doi.org/10.1007/978-3-031-32910-4_4
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