Abstract
Dockless sharing bikes play an increasingly significant role in transit transfer, especially for the first/last mile. However, it is not always accessible for users to find sharing bicycles. The objective of this paper is to assess the accessibility of dockless sharing bikes from a network perspective, which would provide a decision-making basis not only for potential bike users but also for urban planners, policymakers, and bicycle suppliers to optimize sharing-bike systems. Considering bicycle travel characteristics, a hierarchical clustering algorithm was applied to construct the dockless sharing-bike network. The social network analysis (SNA) method was adopted to assess the accessibility of the bike network. Then, a spatial interaction model was chosen to conduct a correlation analysis to compare the accessibility obtained from the SNA approach. The case study of Shanghai indicates a strong connection between the accessibility and the SNA indicators with the correlation coefficient of 0.779, which demonstrates the feasibility of the proposed method. This paper contributes to a deep understanding of dockless sharing-bike network accessibility since the SNA approach considers both the interaction barriers and the network structure of a bicycle network. The developed methodology requires fewer data and is easy to operate. Thus, it can serve as a tool to facilitate the smart management of sharing bikes for improving a sustainable transportation system.
Highlights
Dockless bike-sharing has been launched in many counties worldwide since 2015
There is still some room for improvements reflected in the dockless sharing-bike satisfaction survey. e biggest problem is that when passengers want to use sharing bikes to travel, it is difficult to find sharing bikes within the acceptable range. is indicates that it is extremely important for dockless sharing bikes to be accessible to the potential users, as accessibility is a key indicator to evaluate the effective functioning of dockless sharing bikes. erefore, to improve the satisfaction of dockless bike-sharing service, the accessibility of dockless sharing bikes should be assessed first to inform both suppliers and users
Each zone is a convex polygon with an area of about 0.25 km2 (Figure 4). Comparing these clustering centers with points of interest (POI), we could find that most of them are coincident with POIs, indicating that bikes spontaneously gather around POIs. e division of bike traffic zones lays a foundation for the follow-up research of this paper and provides a new idea for dealing with the network distribution of nonmotorized traffic
Summary
Dockless bike-sharing has been launched in many counties worldwide since 2015. A still-growing list of cities that provide such service can be found at the bike-sharing world map. Dockless bike-sharing has sprung up in China since 2016, and the scale of shared bicycle users in China reached 235 million in 2018, indicating its extensive usage among urban transportation. Erefore, to improve the satisfaction of dockless bike-sharing service, the accessibility of dockless sharing bikes should be assessed first to inform both suppliers and users. Is paper focuses on measuring the accessibility of dockless sharing bikes in a network perspective within relatively available data. We apply the social network analysis approach to the evaluation of the bicycle network, and a correlation analysis between accessibility and SNA indicators is conducted to verify the effectiveness and reliability of the proposed method.
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