Unravel the spatio-temporal patterns and their nonlinear relationship with correlates of dockless shared bikes near metro stations

ABSTRACT

The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation. However, higher flexibility may cause an imbalance between supply and demand during daily operation, especially around the metro stations. A stable and efficient rebalancing model requires spatio-temporal usage patterns as fundamental inputs. Therefore, understanding the spatio-temporal patterns and correlates is important for optimizing and rescheduling bike-sharing systems. This study proposed a dynamic time warping distance-based two-dimensional clustering method to quantify spatio-temporal patterns of dockless shared bikes in Wuhan and further applied the multiclass explainable boosting machine to explore the main related factors of these patterns. The results found six patterns on weekdays and four patterns on weekends. Three patterns show the imbalance of arrival and departure flow in the morning and evening peak hours, while these phenomena become less intensive on weekends. Road density, living service facility density and residential density are the top influencing factors on both weekdays and weekends, which means that the comprehensive impact of built-up environment attraction, facility suitability and riding demand leads to the different usage patterns. The nonlinear influence universally exists, and the probability of a certain pattern varies in different value ranges of variables. When the densities of living facilities and roads are moderate and the relationship between job and housing is relatively balanced, it can effectively promote the balanced usage of dockless shared bikes while maintaining high riding flow. The spatio-temporal patterns can identify the associated problems such as imbalance or lack of users, which could be mitigated by corresponding solutions. The relative importance and nonlinear effects help planners prioritize strategies and identify effective ranges on different patterns to promote the usage and efficiency of the bike-sharing system.

KEYWORDS:

• Dockless shared bikes
• spatio-temporal patterns
• machine learning
• transit oriented development

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Restrictions apply to the availability of these data. The data are not publicly available due to Mobike’s contract and privacy policy.

Additional information

Funding

This research is supported by the National Key Research and Development Program of China [grant number 2017YFB0503601]

Notes on contributors

Zhaomin Tong

Zhaomin Tong is a PhD candidate in School of Resource and Environmental Science, Wuhan University, China. His research interests include geospatial analysis, spatio-temporal behavior and complex urban system.

Yi Zhu

Yi Zhu is an assistant engineer in Chengdu Land Planning and Cadastral Affairs Center, Chengdu, China. He got his master’s degree from School of Resource and Environmental Science, Wuhan University. His research mainly focuses on spatial quantitative analysis and urban design.

Ziyi Zhang

Ziyi Zhang received the PhD degree from Wuhan University. Her main research interests and publications are on ecological remote sensing.

Rui An

Rui An is a PhD student at School of Resource and Environmental Science, Wuhan University, China. His research interests include land-use modeling, spatial data aggregation and travel behavior. He is also interested in geospatial analysis.

Yaolin Liu

Yaolin Liu is a professor at School of Resource and Environmental Science, Wuhan University, China. He specializes in spatial modeling.

Meng Zheng

Meng Zheng is the director of Wuhan Transportation Development Strategy Institute. His main research interest is sustainable urban transportation.