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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 32, Number 12(4) (2020)
Copyright(C) MYU K.K.
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  This work is licensed under a Creative Commons Attribution 4.0 International License. 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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