摘要
为深入分析建成环境对家庭出行频率的影响机理,考虑可塑性面积单元问题(Modified Areal Unit Problem,MAUP)效应,建立泊松回归模型分析不同空间尺度(400 m、800 m及1600 m缓冲区)建成环境对家庭出行频率的影响,并基于北京市居民出行调查数据进行实证研究.结果表明:不同缓冲区建成环境对出行频率的影响不尽相同,表明建成环境对出行行为影响关系中存在MAUP效应;基于400 m缓冲区建成环境的模型拟合效果最优;居住地道路交叉口密度和到城市子中心距离对出行频率具有显著的负向影响;道路密度和到公共交通站点距离对出行频率的影响则呈显著的正效应;公共交通站点密度和土地利用混合度对出行频率不具有显著影响.
Aiming to explore the influence mechanism of built environment on household travel behavior,a Poisson regression model is applied to examine the impacts of built environment on household travel frequency considering modified areal unit problem(MAUP)effects.Travel survey data from Beijing is used to conduct an empirical study by considering the built environment at different spatial buffers(i.e.,400 m,800 m and 1600 m).The results suggest that the built environment characteristics at different spatial buffers show different impacts on household travel frequency,which confirm the MAUP effects.Moreover,the model based on the built environment measured at 400 m buffers shows the best performance.Specifically,the intersection density and distance to city subcenter show negative impacts on travel frequency,whereas the street density and distance to public transit stops show positive impacts on travel frequency.The impacts of public transit stops and land use miXare not significant.
作者
王晓全
邵春福
尹超英
郑长江
WANG Xiaoquan;SHAO Chunfu;YIN Chaoying;ZHENG Changjiang(College of Civil and Transportation Engineering,Hohai University,Nanjing 210098,China;School of Traffic and Transportation Engineering,Xinjiang University,urumqi 830017,China;Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100422,China;College of Automobile and Traffic Engineering,Nanjing Forestry University,Nanjing 210037,China)
出处
《交通工程》
2023年第6期21-26,共6页
Journal of Transportation Engineering
基金
国家自然科学基金项目(52202388,72204114,52072025)
中国博士后面上项目(2022M720992)
教育部人文社科青年基金项目(22YJC630191).
关键词
交通工程
建成环境
MAUP效应
出行频率
泊松回归模型
traffic engineering
built environment
MAUP effects
travel frequency
Poisson regression model
