为准确把握建筑分布对站点客流的影响规律,本文采用以建筑楼栋为单位的建筑普查数据替代既有研究中的POI(Point of Interest)数据,同时考虑建筑混合度、换乘线路数、周边公交站点数、周边道路里程等多种可能因素影响,并在初步回归分析剔除道路网络密度等无明显相关性因素后,建立考虑空间异质性的局域空间回归模型(GWR),研究建筑总量与轨道站点客流量的关系.之后通过控制换乘线路数、周边公交站点数等因素对站点客流的影响,建立对样本量需求较小的全局性空间回归模型(SLM)分析不同类型建筑对轨道站点客流量的影响.研究结果表明:建筑总量与站点客流量呈正相关关系,其在500,800,1000 m范围内相关系数分别为0.015、0.007和0.004,建筑混合度与站点客流量相关性不显著;在各类建筑中,工业建筑和综合建筑对轨道交通客流量影响不明显,其余建筑均与站点客流量呈正相关,且相关性由高到低分别为私宅、办公、商业、住宅;随着与站点距离的增加,商业建筑相关性衰减最快,其次是私宅、办公建筑,超过800 m范围后商业建筑与站点客流量相关性不显著.
Impact of Building Distribution Around Rail Stations on Passenger Flow
To accurately understand the influence of building distribution on station passenger flow,building census data based on building units was used instead of the POI(Point of Interest)data from existing studies.This approach considers various potential influencing factors such as building diversity,the number of transfer lines,the number of surrounding bus stops,and surrounding road mileage.After eliminating factors like road network density that show no significant correlation through preliminary regression analysis,a Geographically Weighted Regression(GWR)model that considers spatial heterogeneity was established to study the relationship between the total number of buildings and rail station passenger flow.Subsequently,by controlling factors such as the number of transfer lines and surrounding bus stops that affect station passenger flow,a Spatial Lag Model(SLM)that requires a smaller sample size was employed to analyze the impact of different types of buildings on rail station passenger flow.The study results indicate a positive correlation between the total number of buildings and station passenger flow,with correlation coefficients of 0.015,0.007,and 0.004 at ranges of 500 meters,800 meters,and 1000 meters,respectively.The correlation between building diversity and station passenger flow is not significant.Among various types of buildings,industrial and mixed-use buildings do not significantly impact rail transit passenger flow,whereas other buildings show a positive correlation with station passenger flow.The correlation rank from highest to lowest is private residences,office,commercial,and residential buildings.As the distance from the station increases,the correlation of commercial buildings decreases most rapidly,followed by private residences and office buildings.Beyond 800 meters,the correlation between commercial buildings and station passenger flow is not significant.
urban trafficentry and exit passengersGWR model and SLM modelbuilding volumebuilding mix
万方数据
维普
