The spatial and temporal distribution characteristics of metro passenger flow have a strong correlation with the land use and spatial structure around the network,and therefore it is important to clarify the spatial and temporal heterogeneity of the built environment's influence on passenger flows.Spatio-temporal heterogeneity refers to the differences that exist in different spatio-temporal states.This thesis takes the Hangzhou metro as an example,combines the mining and application of urban multi-source data,constructs a built environment factor set,establishes four station clustering models based on the Gaudet point distribution method,combines the results of the optimal clustering,continues to refine the spatio-temporal dimensions based on the spatio-temporal geographically weighted regression model,and draws conclusions on the influence of six built environment factors on passenger flows during May Day holidays and National holidays on a daily basis and at each type of station,and analyzes the causes.
关键词
车站聚类/直接吸引范围/城市数据挖掘/时空地理加权回归模型/建成环境
Key words
station clustering/range of direct attraction/urban data mining/spatio-temporal geographical weighted regression model/built environment
维普
万方数据