吉林大学学报(工学版) ›› 2020, Vol. 50 ›› Issue (2): 543-548.doi: 10.13229/j.cnki.jdxbgxb20181087

• 交通运输工程·土木工程 • 上一篇    

考虑空间异质性的建成环境对通勤方式选择的影响

尹超英(),邵春福(),王晓全,熊志华   

  1. 北京交通大学 综合交通运输大数据应用技术交通运输行业重点实验室，北京 100044
  • 收稿日期:2018-11-20 出版日期:2020-03-01 发布日期:2020-03-08
  • 通讯作者: 邵春福 E-mail:15114226@bjtu.edu.cn;cfshao@bjtu.edu.cn
  • 作者简介:尹超英（1989-），女，博士研究生.研究方向：交通与土地利用.E-mail: 15114226@bjtu.edu.cn
  • 基金资助:
    国家自然科学基金创新研究群体项目(71621001);国家自然科学基金项目(51678044)

Influence of built environment on commuting mode choice considering spatial heterogeneity

Chao-ying YIN(),Chun-fu SHAO(),Xiao-quan WANG,Zhi-hua XIONG   

  1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China
  • Received:2018-11-20 Online:2020-03-01 Published:2020-03-08
  • Contact: Chun-fu SHAO E-mail:15114226@bjtu.edu.cn;cfshao@bjtu.edu.cn

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摘要：

为探讨交通小区层面建成环境各因素对居民通勤方式选择的影响，构建了考虑空间异质性的多层logistic模型，以长春市为例开展了实证研究。模型同时考虑了个体层面和交通小区层面变量对通勤行为的影响，并借助HLM软件标定模型参数。结果表明：居民通勤方式选择的空间异质性是显著存在的；在控制了空间异质性后，个体层面变量中性别、年龄、教育水平、户口类型、家庭收入、家庭小汽车拥有和出行属性中的通勤距离均对居民通勤方式选择有显著影响；交通小区层面中，居住地土地利用混合度越高、公共交通站点及道路交叉口密度越大，居民选择小汽车通勤的可能性越低，而居住在距离城市中心商务区较远的居民更倾向选择小汽车通勤。

关键词: 交通运输系统工程, 建成环境, 通勤方式选择, 空间异质性, 多层logistic模型

Abstract:

In order to explore the influences of built environment characteristics on commuting mode choice, a multilevel binary logistic model considering spatial heterogeneity is establsihed, and Changchun is chosen as the empirical case in this study. The influences of the variables at individual level and traffic analysis zone level are considered simultaneously in the model. The model parameters are calibrated using HLM software. The result shows that the spatial heterogeneity of commuting mode choice significantly exists. At the individual level, gender, age, education, hukou, household income, household car ownership and commuting distance have significant influences on commuting mode choice. At the traffic analysis zone level, residents living in areas with higher land use mix, transit station and intersection density have a lower probability of commuting by car. Additionally, residents living farther from Central Business District (CBD) tend to have a higher probability of driving to work.

Key words: engineering of communications and transportation system, built environment, commuting mode choice, spatial heterogeneity, multilevel logistic model

中图分类号: 

  • U491

表1

变量特征和描述性统计"

类 别变 量变量描述均值标准差
个体层性别1=男；0=女0.5800.493
年龄连续变量37.12010.155
教育水平1=大学及以上；0=大学以下0.3500.478
户口类型1=本地户口；0=非本地户口0.9400.230
家庭收入

收入1：1=小于等于2万元；0=其他

收入2：1=大于10万元；0=其他

0.030

0.130

0.177

0.340

家庭小汽车拥有连续变量0.2600.475
通勤距离连续变量5.62510.266
通勤方式1=小汽车出行；0=其他出行方式0.1600.367
交通小区层土地利用混合度交通小区11类兴趣点的混合程度0.5890.066
公共交通站点密度公共交通站点数量/交通小区面积10.5475.242
到CBD距离交通小区质心到CBD的距离5.0592.446
道路交叉口密度道路交叉口数量/交通小区面积29.42427.220

图1

研究区域及交通小区划分"

图2

个人与交通小区的分层数据结构"

表2

模型参数标定"

类 别变量名称单层模型多层模型
系数P系数P
个体层性别1.9360.0001.2480.000
年龄0.1020.0610.1770.000
教育水平0.8590.0001.2290.000
户口类型0.1180.6120.2960.031
家庭规模0.0580.2670.0470.231
家庭收入1-0.0130.087-0.1110.074
家庭收入20.0540.0890.1320.082
家庭小汽车拥有6.4870.0001.4430.000
通勤距离0.0150.0030.0080.000
交通小区层土地利用混合度-1.2650.069-2.1250.056
公共交通站点密度-0.0150.060-0.0250.071
到CBD距离0.0140.1710.3120.066
道路交叉口密度-0.0010.096-2.0050.034
AIC值3 189.6083 014.336
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