Download pdf
Optimization of Bike-Sharing Stations in Manhattan
- 1 School of Pinghe, Shanghai, Postcode:201206, China
* Author to whom correspondence should be addressed.
Abstract
Whether for environmental protection or as a means of transportation, the use of shared bicycles has been expanding. This has also brought about the management problem of shared bicycles and how to optimize the placement of stations. The main goal of this paper is to optimize the location of shared bicycle stations during the peak hours of shared bicycle use in Manhattan so that shared bicycle companies can achieve more effective placement at the minimum cost. The method used can be divided into three parts. First, a grid is established to simulate the layout and construction of the city, assuming the values of the variables; the second part establishes a linear function, modifies the objective function for different variable factors, and applies it to the grid of the first part to test whether it is the optimal placement location; after verification, the third part combines the model with the actual situation in Manhattan and calculates 10 optimal locations for the placement of shared bicycle stations. This paper determines the optimal way to place stations by minimizing costs and combining actual needs, thereby improving the effectiveness and timeliness of future shared bicycle rentals.
Keywords
optimize, bike-sharing stations, Manhattan, linear function
[1]. Beigi, Pedram, Khoueiry, Michel, Rajabi, Mohammad Sadra and Hamdar, Samer. (2022). Station Reallocation and Rebalancing Strategy for Bike-Sharing Systems: A Case Study of Washington DC [Unpublished master's thesis]. The George Washington University.
[2]. Eoin O’Mahony1, David B. Shmoys1,2. (2015). Data Analysis and Optimization for (Citi)Bike Sharing. https://file:///Users/lichenyi/Downloads/9245-Article%20Text-12773-1-2-20201228.pdf
[3]. Hamidreza Sayarshad, Sepideh Tavassoli, Fang Zhao. (2011). A multi-periodic optimization formulation for bike planning and bike utilization [Unpublished doctoral dissertation]. Mazandaran University, Azad University, Florida International University.
[4]. Juan P. Romero, Angel Ibeas, Jose L. Moura*, Juan Benavente, Borja Alonso. (2012). A simulation-optimization approach to design efficient systems of bike-sharing [Unpublished master's thesis]. University of Cantabria, Avenida Los Castros s/n, Santander 39005, Spain.
[5]. Edoardo Croci and Davide Rossi. (2014). Optimizing the position of bike sharing stations. The Milan case [Unpublished doctoral dissertation]. IEFE ‐ The Center for Research on Energy and Environmental Economics and Policy at Bocconi University.
[6]. Suleyman Mete, Zeynel Abidin Cil, Eren Özceylan. (2018). Location and Coverage Analysis of BikeSharing Stations in University Campus [Unpublished master's thesis]. Munzur University, Izmir Democracy University, Gaziantep University.
[7]. Zhihui Gu, Yong Zhu, Yan Zhang, Wanyu Zhou, Yu Chen. (2019). Heuristic Bike Optimization Algorithm to Improve Usage Efficiency of the Station-Free Bike Sharing System in Shenzhen, China [Unpublished doctoral dissertation]. College of Architecture and Urban Planning, Shenzhen University.
[8]. Zhuo Sun, Yiming Li, Yi Zuo. (2019). Optimizing the Location of Virtual Stations in Free-Floating Bike-Sharing Systems with the User Demand during Morning and Evening Rush Hours [Unpublished doctoral dissertation]. Dalian Maritime University.
[9]. Yujie Hu, Yongping Zhang, David Lamb, Mingming Zhang, Peng Jia. (2019). Examining and optimizing the BCycle bike-sharing system – A pilot study in Colorado, US [Unpublished doctoral dissertation]. University of South Florida, University College London, University of South Florida, Rice University, University of Twente.
[10]. (n.d.). Manhattan Population Explorer. https://manpopex.us/
[11]. Manhattan bike paths, bike lanes & Greenways. (n.d.). NYC Bike Maps | New York City Bicycle Maps. https://www.nycbikemaps.com/maps/manhattan-bike-map/
[12]. Manhattan shared bike station. (n.d.). Manhattan shared bike station. https://www.google.com/maps/search/manhattan+shared+bike+station/@40.7525516,-74.0257411,10686m/data=!3m2!1e3!4b1?entry=ttu
[13]. Yunzhe Liu, Daniel Arribas-Bel, Meixu Chen. (2021). Profiling the Dynamic Pattern of Bike-sharing Stations: a case study of Citi Bike in New York City [Unpublished doctoral dissertation]. University of Oxford, University of Liverpool.
Cite this article
Li,C. (2025). Optimization of Bike-Sharing Stations in Manhattan. Theoretical and Natural Science,107,41-49.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 4th International Conference on Computing Innovation and Applied Physics
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).