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

Shang-Yuan Chen; Tzu-Tien  Chen

doi:10.46604/aiti.2021.6666

Authors

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

DOI:

https://doi.org/10.46604/aiti.2021.6666

Keywords:

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

Abstract

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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