Understanding the usage of dockless bike sharing in Singapore

ABSTRACT

A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.

KEYWORDS:

• Built environment
• data mining
• dockless/stationless bike sharing
• GPS data
• spatiotemporal analysis

Acknowledgement

The research is supported by the National Research Foundation (NRF), Prime Minister's Office, Singapore, under CREATE programme, Singapore-MIT Alliance for Research and Technology (SMART) Centre, Future Urban Mobility (FM) Interdisciplinary Research Group. We thank the anonymous reviewers whose comments and suggestions helped improve and clarify this manuscript.

Notes

¹ Romanillos et al. (2016) have summarized a series of studies using GPS data from smartphone apps to investigate cycling behaviors. Those studies collected their GPS data by either recruiting a group of participating cyclists or acquiring data directly from smartphone app companies (e.g., fitness apps). Nevertheless, these data are not suitable for studies of bike sharing and do not necessarily reflect the utilization of bike-sharing services.

² In this paper, we use MRT to refer to both MRT and LRT (light-rail transit) LRT systems for simplification.

³ There were also 30-minute free ride promotions during the weekend.