Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (3): 572-577.doi: 10.13229/j.cnki.jdxbgxb20200844

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Investigating influences of multi⁃scale built environment on car ownership behavior based on gradient boosting decision trees

Chao-ying YIN1(),Chun-fu SHAO2(),Zhao-guo HUANG3,Xiao-quan WANG2,Sheng-you WANG2   

  1. 1.College of Automobile and Traffic Engineering，Nanjing Forestry University，Nanjing 210037，China
    2.Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport，Beijing Jiaotong University，Beijing 100044，China
    3.School of Civil Engineering，Lanzhou University of Technology，Lanzhou 730050，China
  • Received:2020-11-02 Online:2022-03-01 Published:2022-03-08
  • Contact: Chun-fu SHAO E-mail:cyyin@njfu.edu.cn;cfshao@bjtu.edu.cn

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Abstract:

In order to quantify the influences of the built environment at different spatial scales on car ownership， a gradient boosting decision tree model is applied to investigate the relative importance of the built environment at both city and community levels in this study. The survey data from China Labor-force Dynamics Survey is used to conduct the empirical analysis. The results show that the individual socio-economic status is the most influential among three categories of factors with collective importance of 53.24%. With the collective importance of 30.45%， the built environment characteristics at the community level have greater influences than the built environment characteristics at the city level. For the specific characteristics， household income is the most influential factor with the relative importance of 31.66%. All built environment characteristics have influences that are greater than 1.5%. Therefore， it is important to optimize the built environment at different spatial scales to deter the increase of car ownership.

Key words: engineering of communicaiton and transportation system, built environment, car ownership, different spatial scales, gradient boosting decision tree

CLC Number: 

  • U491

Table 1

Statistical description of the explanatory variables"

变量名称变量描述均值方差
个体层-社会经济属性性别1=男性；0=女性0.510.50
年龄连续变量45.3513.17
教育水平1=小学及以下；2=初中；3=高中；4=大学及以上2.121.10
16岁以下孩子数量家庭中16岁以下孩子数量0.610.83
家庭规模家庭人口数量3.291.48
家庭收入家庭年收入（单位：万元）6.2411.63
社区层-建成环境特征社区人口密度社区内人口数量与社区行政面积之比（单位：万人/km2）0.344.89
土地利用混合度基于运动设施、图书室、广场及银行等4类设施计算得到的土地利用混合度0.430.36
到公共交通站点距离到最近的公共交通站点的距离（单位：km）2.456.08
到CBD距离到城市中心商务区的距离（单位：km）5.387.73
运动设施数量社区内运动设施数量1.923.20
图书室数量社区内图书室数量1.101.23
广场数量社区内广场数量0.881.79
银行数量社区内银行数量1.172.84
城市层-建成环境特征城市人口密度城市常住人口数量与城市建成区面积之比（单位：万人/km2）0.210.13
是否有地铁1=是；0=否0.230.43
每万人公交车辆数每万人拥有公共交通车辆数（单位：辆/万人）2.173.33
人均道路面积城市道路面积与城市常住人口之比（单位：平方米/人）5.497.14

Table 2

Relative importance and ranking of explanatory variables"

变量名称重要度排序影响程度/%
个体层-社会经济属性性别180.20
年龄46.31
教育水平29.23
是否有16岁以下孩子171.02
家庭规模64.82
家庭收入131.66
合计53.24
社区层-建成环境特征是否有运动设施132.80
是否有图书室142.44
是否有广场152.38
是否有银行113.66
土地利用混合度84.73
到公共交通站点距离104.49
到CBD距离123.29
社区人口密度36.66
合计30.45
城市层-建成环境特征城市人口密度94.66
是否有地铁161.66
每万人公交车辆数74.77
人均道路面积55.22
合计16.31
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[1] Chao-ying YIN,Chun-fu SHAO,Xiao-quan WANG,Zhi-hua XIONG. Influence of built environment on commuting mode choice considering spatial heterogeneity [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 543-548.
[2] Chao⁃ying YIN,Chun⁃fu SHAO,Xiao⁃quan WANG. Influence of urban built environment on car commuting considering parking availability [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 714-719.
[3] Ge Feng-hua, Liu Xun-jun, Liu Ye, Wang Yue-zhi . Air pollution and natural ventilation in underground car park [J]. 吉林大学学报(工学版), 2007, 37(03): 696-0700.
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