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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
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Sensors and Materials, Volume 32, Number 12(4) (2020)
Copyright(C) MYU K.K.
pp. 4463-4470
S&M2422 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.3125
Published: December 29, 2020

Location Optimization of Bicycle-sharing Stations Using Multiple-criteria Decision Making [PDF]

Tae-Young Lee, Myeong-Hun Jeong, Seung-Bae Jeon, and Jae-Myoung Cho

(Received September 28, 2020; Accepted December 9, 2020)

Keywords: bicycle-sharing system, location-allocation models, network analysis, multiple linear regression, MCDM

A bicycle-sharing system not only allows citizens to freely use bicycles installed in specific locations but is also a supplement to public transportation. In this study, we aim to improve the accessibility to public bicycles by finding the optimal locations of bicycle-sharing stations based on the history and spatial data of public bicycle operations. IoT sensors record the bicycles’ movement information. Multiple linear regression analysis is used to select the most important criteria for a bicycle-sharing system. The selected criteria are then applied to multiple-criteria decision making (MCDM) to rank the potential locations of bicycle-sharing stations. The top-ranking locations are finally determined through an optimization stage.

Corresponding author: Myeong-Hun Jeong


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Cite this article
Tae-Young Lee, Myeong-Hun Jeong, Seung-Bae Jeon, and Jae-Myoung Cho, Location Optimization of Bicycle-sharing Stations Using Multiple-criteria Decision Making, Sens. Mater., Vol. 32, No. 12, 2020, p. 4463-4470.



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