铁道运输与经济2024，Vol.46Issue(2)：159-166,8.DOI:10.16668/j.cnki.issn.1003-1421.2024.02.20
基于PCA-GWR方法探究建成环境对轨道站点客流的影响
Exploration of Impact of Built Environment on Passenger Flow of Rail Stations Based on PCA-GWR Method
摘要
Abstract
The passenger attraction capacity of rail stations is closely related to the surrounding built environment conditions.This paper focused on the built environment surrounding rail stations,including the identification of walking reachable ranges,extraction and quantitative description of 5Ds built environment elements,and the selection and analysis of relevant passenger flow models,forming a data-driven approach for modeling the built environment and rail passenger flow quantification.The case study utilized rail stations in Chongqing as examples.The research findings are as follows.① The area within a 10-minute walk is 74.2% of the buffer area with a radius of 500 m.② Principal component explanatory variables can overcome the issue of multicollinearity between explanatory variables.The principal component-based geographic weighted regression(PCA-GWR)model performs better in fitting than the geographic weighted regression model.③ Residential richness,travel convenience,greenery effect,station accessibility,and rail station egress passenger flow are closely related.The influence of principal component explanatory variables on rail station egress passenger flow varies significantly with spatial changes.关键词
轨道站点/步行等时圈/主成分分析/地理加权回归/网络数据提取Key words
Rail Stations/Walking Reachable Range/Principal Component Analysis/Geographic Weighted Regression/Network Data Extraction分类
交通工程引用本文复制引用
李毅军,罗紫宇,周涛,张振豪..基于PCA-GWR方法探究建成环境对轨道站点客流的影响[J].铁道运输与经济,2024,46(2):159-166,8.基金项目
重庆英才计划项目(CQYC20210207147) （CQYC20210207147）
