Analyzing the micro-mobility patterns of shared dockless bike and e-bike systems through multi-scale complex networks

Micro-mobility is an emerging mode of urban transportation, particularly for its potential to reduce private car use and build sustainable cities. Bikes and electric bikes (e-bikes) are two common types of micro-mobility. Previous studies mainly analyzed the two types separately, failing to provide a comparative and systematic investigation of shared dockless bike/e-bike systems in different cities. To fill this gap, this paper presents a multiscale geospatial network framework to analyze the differences between bike and e-bike usage patterns, taking five cities with different urban contexts in Zhejiang Province (China) as examples. We first organize the shared micro-mobility data into bike networks and e-bike networks. Then, we analyze these networks from the macro, meso, and micro scales by applying complex network analysis and data mining techniques. A series of visual graphs are designed to help understand latent patterns. The similarities and differences among the five cities in statistical information, visitation hotspots, micro-mobility network indicators, and community structures are clearly demonstrated. The results can enrich our understanding of the system usage patterns, and help micro-mobility operators and transportation planners make evidence-based policies to promote sustainable urban development.