Revealing Travel Patterns of Sharing-Bikes in a Spatial-Temporal Manner Using Non-Negative Matrix Factorization Method

The booming bike-sharing business provides great convenience to people's daily travel and brings notable change to city traffic. Meanwhile, few studies have analyzed the basis patterns of sharing-bikes and their influence on the traffic using empirical data. In this paper, we investigate the data provided by one of the leading bike-sharing companies. The city is gridded into regular rectangle regions using a geohash algorithm, and the starting and ending region of bike journeys are given. We modeled the macro travel patterns of sharing bikes in a spatial-temporal manner and used the non-negative matrix factorization (NFM) method to obtain the basis collective patterns. The patterns show that, different from other modes of shared mobility like ride-sharing, the trip characteristics of bike-sharing could be approximately described by a linear combination of three basis patterns, and bike-sharing mainly serves for the first/last mile short-distance travel around transport hubs, i.e., subway stations. Our findings are helpful for policy makers to better understand the dynamics patterns and influence of sharing-bikes, and to make better arrangements towards facility building and other bike policies.