Abstract
Dockless bike sharing (DBS) provides a sustainable and green travel mode, which also enhances the connections with other travel modes. Understanding the travel mobility and demand of DBS become an urgent task for government and operators to provide better service. In this paper, we propose a network-based method to detect the travel mobility of DBS users based on the actual trip data. The studied area is divided by square grid with same size. The grids with trips are considered as nodes and the connections between nodes are considered as edges. To gain the dynamic characteristics of DBS travel mobility, we construct several networks according to different time periods in a weekday. We build a data-driven framework to analyze DBS network including accessibility, spatial inequality, spatial autocorrelation and network-based indicators. The relationship between flow strength and point-of-interest (POI) is discussed. The results show that travel demands of DBS are higher in morning peak and evening peak on weekdays. The DBS networks are inequality, connections are concentrated on center area. From the network view, the DBS network are assortative and positive autocorrelated with evident communities. The results imply that the number of residence and transport facility have strong correlations with flow strength.
Sign-in/Register to access full text options 
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.
