Dockless Shared Bicycle Flow Control by Using Kernel Density Estimation Based Clustering


  • Shang-Yuan Chen School of Architecture, Feng Chia University, Taichung, Taiwan
  • Tzu-Tien Chen School of Architecture, Feng Chia University, Taichung, Taiwan



classification, clustering, unsupervised neural networks learning, R language, choropleth map


Since dockless sharing bicycles have become an indispensable means of everyday life for urban residents, how to effectively control the supply and demand balance of bikes has become an important issue. This study aims to apply Kernel Density Estimation based (KDE-based) clustering analysis and a threshold-based reverse flow incentive mechanism to encourage the users of bicycles to adjust the supply and demand actively. And it takes Shanghai Jing’an Temple and its surroundings as the research area. Its practical steps include: (1) compilation and processing of the needed data, (2) application of KDE-based clustering, partitioning, and grading, and (3) incentives calculation based on dockless shared bicycle flow control system. The study finds that the generalization function of KDE-based clustering can be used to estimate the density value at any point in the study area to support the calculation of the incentive mechanism for bicycle reverse flow.


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How to Cite

S.-Y. Chen and T.-T. . Chen, “Dockless Shared Bicycle Flow Control by Using Kernel Density Estimation Based Clustering”, Adv. technol. innov., vol. 6, no. 3, pp. 146–156, Apr. 2021.