Modelling the influence of urban morphology on bikeshare station use: a clustering approach
本研究采用聚类方法对英国多个城市的城区进行分类，分析城市形态（如街道布局、土地利用）如何影响共享单车站点使用，为站点选址和需求预测提供数据支持。
Docked bikeshare schemes have proliferated across UK cities since the first scheme was introduced in 2010. These schemes have been widely adopted for their contributions to decarbonising transport, improving health, and enhancing connectivity through first and last-mile trips. As bikeshare expands to new cities, planners and operators increasingly require a localised understanding of the factors influencing bikeshare use. Urban morphology in UK cities varies widely, however, encompassing differences in street layouts, building design, accessibility, and land use. Meanwhile, industry bikeshare planning guidelines are often broad, without distinguishing between city size and character. These variations pose challenges for bikeshare scheme planning in different settings, emphasising the need for robust, data-driven models that are sensitive to urban context. This paper employs cluster analysis to classify urban areas within several UK cities, with the aim to understand the combined contextual urban factors that influence bikeshare use. This approach, rarely applied in micromobility research, offers a nuanced and unique methodological contribution. The cluster analysis distinguishes between types of residential neighbourhoods, which is a component less commonly incorporated within existing studies. With the data obtained, statistical analysis offers granular insights into the relationship between the built environment and docking station use. It is highlighted that denser residential neighbourhoods with favourable accessibility have consistent associations with trip generation, while accessible suburban neighbourhoods are more varied. The findings have implications for both initial planning and scheme expansion, relevant to station location optimisation, forecasting future demand, fleet size adjustment and integration with existing public transport networks.