Assessing Accessibility of Dockless Sharing-Bike Networks by the Social Network Analysis Method

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.

Empathy Theory and Casework Problem Solving Based upon Social Network Analysis

“…The social network analysis (SNA) method was adopted to assess the accessibility of the bike network. Then, a spatial interaction model was cho-sen to conduct a correlation analysis to compare the accessibility obtained from the SNA approach [4]. Abend illustrates the impact of important events in people's lives on his own central compromise structure by giving examples between empathy and countertransference; the result is that empathic emotions must affect people's analytic functions [5].…”

Study on Community Detection Method for Morning and Evening Peak Shared Bicycle Trips in Urban Areas: A Case Study of Six Districts in Beijing

“…To illustrate, Beck et al devised a bicycle network to probe the interrelationship between environmental characteristics and cycling behavior [12]. Liu et al evaluated the accessibility of dockless bike-sharing from a network vantage point, proffering decision-making support for urban planners, policymakers, and bike-sharing providers to fine-tune bike-sharing systems [13]. Hu et al delved into the travel patterns of bike-sharing by constructing a spatial interaction network and juxtaposed the disparities between weekdays and weekends [14].…”