Understanding dockless bike-sharing spatiotemporal travel patterns: Evidence from ten cities in China

Dockless bike-sharing (DBS) has gained popularity in many cities in recent years. However, little attention has been given to the commonalities and differences in DBS travel patterns from multiple cities. This study aims to fill this gap by comparing DBS travel patterns in ten Chinese cities of varying scales. It collected data from these cities and analysed them from two perspectives: service usage characteristics and complex network properties. The results reveal a coexistence of similarities and differences in DBS travel patterns across different cities. The ten major cities show significant similarities in temporal distribution, with a similarity coefficient exceeding 0.7. Southern cities have a higher percentage of night riding compared to their northern counterparts. Moreover, over 85% of the travel distances are within 2 km. Concerning spatial distribution, all ten cities demonstrate an imbalanced demand distribution during peak hours, particularly in downtown areas, exhibiting evident tidal patterns. Network analysis outcomes demonstrate that the DBS networks in all ten cities possess small-world and scale-free properties. Furthermore, nodes with high degree centrality and closeness centrality are predominantly concentrated in downtown areas, displaying a diminishing trend from the city centre to the outskirts and showcasing strong global spatial autocorrelation. The betweenness centrality exhibits a property of random distribution. Nodes with high PageRank values are mainly concentrated in the city centre and around metro stations. The findings offer valuable insights for transportation planners and managers seeking to enhance their understanding of DBS systems.