This paper using the Geographically Weighted Regression model to fit the relationship between built environment variables and subway passenger flow,and analyzes the influence of significant variables on subway passenger flow.Using the passenger flow data of 63 stations of Xi'an Metro Line 1,2 and 3 for 5 working days,and ArcGIS to match the passenger flow with the traffic analysis zones.On the basis of the traditional least squares regression model,constructed the GWR model.Considering the impact of GDP per capita,land use mixing degree,parking lot density,intersection density,subway entrance and exit density,etc.on the passenger flow of subway entrances and exits.The following conclusions can be obtained:The GWR model can depict the spatial non-stationarity and influence scale of the interaction between subway commuter passenger flow and built environment variables,and its results are better than the traditional least squares method.Meanwhile,we found that four variables-GDP per capita,land use mixing degree,intersection density,and subway entrance and exit density-have significant effects on subway passenger flow.The attraction of land-use mixing degree to subway commuter traffic is much greater than the density of subway entrances and exits,and more obvious in traffic analysis zones with low degree of land use development and balance.
关键词
城市交通/建成环境/通勤出行/地铁客流/GWR模型
Key words
urban traffic/built environment/commuting/subway passenger flow/geographically weighted regression model
维普
万方数据