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Operating Characteristics of Dockless Bike-Sharing Systems near Metro Stations: Case Study in Nanjing City, China
With the growth of dockless bike-sharing (DLBS) systems, the first-and-last mile connection to public transport, such as metro and light railway stations, could be improved. DLBS systems complete the trip chain by connecting metro stations with points of interest and enhance the sustainability of urban transportation. Therefore, it is necessary to understand the trans-shipment characteristics of DLBS systems for metro stations. In this study, we collected data from the Mobike DLBS system in Nanjing City, China and applied K-means clustering to analyse the activity patterns of DLBS systems near local metro stations. Metro stations were categorised into five types on workdays and three types on weekends. An analysis of the relationships between activity patterns and spatial distribution characteristics demonstrated that the distribution of clusters possesses a strong connection with the surrounding environment. Low land development rates and a sparse distribution of metro stations cause a large range of influences. This research has direct implications for understanding the operating state of DLBS systems near metro stations and promoting the proper management of DLBS systems.
Operating Characteristics of Dockless Bike-Sharing Systems near Metro Stations: Case Study in Nanjing City, China
With the growth of dockless bike-sharing (DLBS) systems, the first-and-last mile connection to public transport, such as metro and light railway stations, could be improved. DLBS systems complete the trip chain by connecting metro stations with points of interest and enhance the sustainability of urban transportation. Therefore, it is necessary to understand the trans-shipment characteristics of DLBS systems for metro stations. In this study, we collected data from the Mobike DLBS system in Nanjing City, China and applied K-means clustering to analyse the activity patterns of DLBS systems near local metro stations. Metro stations were categorised into five types on workdays and three types on weekends. An analysis of the relationships between activity patterns and spatial distribution characteristics demonstrated that the distribution of clusters possesses a strong connection with the surrounding environment. Low land development rates and a sparse distribution of metro stations cause a large range of influences. This research has direct implications for understanding the operating state of DLBS systems near metro stations and promoting the proper management of DLBS systems.
Operating Characteristics of Dockless Bike-Sharing Systems near Metro Stations: Case Study in Nanjing City, China
Yuan Li (author) / Zhenjun Zhu (author) / Xiucheng Guo (author)
2019
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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