Green travel mobility of dockless bike-sharing based on trip data in big cities: A spatial network analysis

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.

Automated summary

This paper proposes a network-based method to detect the travel mobility of DBS users and analyzes the characteristics of DBS networks using a data-driven framework. The study reveals that travel demands of DBS are higher during morning and evening peaks on weekdays, with network connections concentrated in central areas.