Abstract
The world population will reach 9.8 billion by 2050, with increased urbanization. Cycling is one of the fastest developing sustainable transport solutions. With the spread of public bike sharing (PBS) systems, it is very important to understand the differences between systems. This article focuses on the clustering of different bike sharing systems around the world. The lack of a comprehensive database about PBS systems in the world does not allow comparing or evaluating them. Therefore, the first step was to gather data about existing systems. The existing systems could be categorized by grouping criterions, and then typical models can be defined. Our assumption was that 90% of the systems could be classified into four clusters. We used clustering techniques and statistical analysis to create these clusters. However, our estimation proved to be too optimistic, therefore, we only used four distinct clusters (public, private, mixed, other) and the results were acceptable. The analysis of the different clusters and the identification of their common features is the next step of this line of research; however, some general characteristics of the proposed clusters are described. The result is a general method that could identify the type of a PBS system.
Highlights
According to the UN forecast, the world population in mid-2017 was about 7.6 billion people, and by 2050 it is predicted to reach 9.8 billion
We developed a complete framework during a doctoral research for analyzing, comparing, and categorizing public bike sharing systems, as such a comprehensive system is still missing from the literature [17]
The goal of the clustering process is to create groups of the dataset, in a way that: (i) the objects in a given cluster are similar as much as possible; and (ii) the objects belonging to different clusters are highly different [33].The cluster analysis usually applied in the domain of spatial studies related to public bike sharing (e.g., [34,35,36,37])
Summary
According to the UN forecast, the world population in mid-2017 was about 7.6 billion people, and by 2050 it is predicted to reach 9.8 billion. The goal of the clustering process is to create groups (clusters of objects) of the dataset, in a way that: (i) the objects in a given cluster are similar as much as possible; and (ii) the objects belonging to different clusters are highly different [33].The cluster analysis usually applied in the domain of spatial studies related to public bike sharing (e.g., [34,35,36,37]). In this field, the studies mostly focus on the distribution of bikes or stations. The clustering methodology proposed by the authors contributes, among others, to reducing a large number of primary data to several basic categories that can be treated as subjects for further analysis in the public bike sharing domain
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
