<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Background: Bike-sharing programmes have become popular in a large number of cities in order to facilitate bicycle use. Determining the location of bike sharing stations is vital to success of these programmes. Objectives: In this paper, a case study is applied to the Gaziantep University campus in order to find possible locations of the stations for users (students). The purpose is to minimize the total walking distance. Methods/Approach: Set and maximal covering mathematical models are considered to decide on coverage capability of determined 20 demand points and 20 potential bike stations. Then, the mathematical models of P-center and P-median are used to build possible stations and to allocate demand points to the opened stations. Finally, an undesirable facility location model is used to find the bike stations, which have the maximum distance from demand nodes, and to eliminate them. Results: In computational results, it is clearly seen that the proposed approaches set the potential bike station covering all demand points. They also provide different solutions for the campus planners. Conclusions: The methodology outlined in this study can provide university administrators with a useful insight into locations of stations, and in this way, it contributes significantly to future planning of bike-sharing systems.</p></abstract>

Background: Bike-sharing programmes have become popular in a large number of cities in order to facilitate bicycle use. Determining the location of bike sharing stations is vital to success of these programmes. Objectives: In this paper, a case study is applied to the Gaziantep University campus in order to find possible locations of the stations for users (students). The purpose is to minimize the total walking distance. Methods/Approach: Set and maximal covering mathematical models are considered to decide on coverage capability of determined 20 demand points and 20 potential bike stations. Then, the mathematical models of P-center and P-median are used to build possible stations and to allocate demand points to the opened stations. Finally, an undesirable facility location model is used to find the bike stations, which have the maximum distance from demand nodes, and to eliminate them. Results: In computational results, it is clearly seen that the proposed approaches set the potential bike station covering all demand points. They also provide different solutions for the campus planners. Conclusions: The methodology outlined in this study can provide university administrators with a useful insight into locations of stations, and in this way, it contributes significantly to future planning of bike-sharing systems.

Location and Coverage Analysis of Bike- Sharing Stations in University Campus

Business Systems Research Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Location and Coverage Analysis of Bike- Sharing Stations in University Campus"}

Background: Bike-sharing programmes have become popular in a large number of cities in order to facilitate bicycle use. Determining the location of bike...

Location and Coverage Analysis of Bike- Sharing Stations in University Campus

Online veröffentlicht: 28. Juli 2018

Seitenbereich: 80 - 95

Eingereicht: 21. Dez. 2017

Akzeptiert: 18. Jan. 2018

DOI: https://doi.org/10.2478/bsrj-2018-0021

Schlüsselwörter
Bike station, location-allocation, maximal covering, p-center, p-median, set covering, undesirable facility location

© 2018 Suleyman Mete, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Location and Coverage Analysis of Bike- Sharing Stations in University Campus

Online veröffentlicht: 28. Juli 2018

Seitenbereich: 80 - 95

Eingereicht: 21. Dez. 2017

Akzeptiert: 18. Jan. 2018

DOI: https://doi.org/10.2478/bsrj-2018-0021

SchlüsselwörterBike station, location-allocation, maximal covering, p-center, p-median, set covering, undesirable facility location

© 2018 Suleyman Mete, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Bike station, location-allocation, maximal covering, p-center, p-median, set covering, undesirable facility location