Analysis of Behaviour Pattern of Bicycle-sharing Users Based on Complex Network: a Case Study of Beijing Downtown Area

Abstract. The introduction of the Bicycle-sharing System (BSS) has provided great convenience for residents for short travel in city. However, the parking lot planning and bicycle logistics distribution remain suboptimal, adversely affecting traffic order. Studying the spatial and temporal usage patterns can be helpful for better management of BSS. In this paper, we propose an analytical framework for detecting the bicycle-sharing pattern based on the community detection algorithms from complex networks to study the spatial-temporal patterns of usage.
Firstly we identify the potential demand for bicycle among urban residents with a dataset from the BSS in Beijing downtown area. Then we construct a linkage network for modelling the BSS by applying community detection algorithms to identify regions of both high and low connectivity. Following this, based on the division of sub-areas, three indicators (graph density, cross-area travel demand, and confidence ellipses) are established for characterizing these sub-areas. Our findings include: (1) The usage pattern of the BSS within the downtown area consistently exhibits relatively stable clustering phenomena over time. (2) The usage pattern is related to the urban spatial structure, with significant differences between weekdays and weekends. (3) Residents tend to complete their cycling within the current sub-areas. (4) Generally, smaller sub-areas tend to have denser bicycle travel behaviour. These insights are vital for improving BSS parking lot planning and logistics distribution.