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A Data-Driven Optimization Computational Tool Design for Bike-Sharing Station Distribution in Small to Medium-Sized Cities: A Case Study for Cuenca, Ecuador

Fernando Cueva (), Pengcheng Shi () and Priscila Cedillo ()
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Fernando Cueva: Rochester Institute of Technology
Pengcheng Shi: Rochester Institute of Technology
Priscila Cedillo: Universidad de Cuenca

A chapter in Advances in Information Systems Development, 2022, pp 1-18 from Springer

Abstract: Abstract Faced with heavy vehicular traffic at present, the strategic implementation of Bike Sharing Systems (BSSs) in cities as an alternative means of transport for users is increasingly being adopted. These solutions reduce the environmental burden posed by other means of transportation, decrease costs for citizens, improves people's health due to physical activity, among other advantages. However, aspects such as the definition of bike stations’ locations represent a challenge when these solutions are being implemented. Therefore, this paper presents a software tool design that supports a method that defines the location and number of stations within a BSS. Also, the tool uses a data-driven optimization model to establish the location of stations. Finally, a case study carried out in Cuenca—Ecuador, demonstrates the proposal's feasibility, showing a significant concordance with the consulting firms-consortia results (70–90% of coincidence) at a lower cost.

Keywords: Bike sharing system; Public bike system; Optimization; Pollutants; Data-driven (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-95354-6_1

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DOI: 10.1007/978-3-030-95354-6_1

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