考虑空间自相关的建成环境对通勤方式选择的影响

doi:10.13229/j.cnki.jdxbgxb.20210958

Abstract

Abstract:

Considering the similarity in residents ′ commuting behavior living in nearby zones， several multilevel Bayesian models are employed to examine the impacts of built environment at traffic analysis zone （TAZ） levels on commuting mode choice. Bayesian models can capture the spatial autocorrelation of commuting behavior by incorporating adjacency matrixes to represent the spatial relationships between TAZs. The results show that the spatial autocorrelation significantly exists in residents′ commuting behavior. In addition， the model incorporating a centroid distance adjacent matrix has the best performance among the compared models. After controlling for socioeconomic characteristics at the individual levels， built environment characteristics are important factors of car commuting. Specifically， land use mix， public transit station density and intersection density have negative impacts on commuting by car. These results suggest that increasing the number of public transit stations， promoting more balanced land use and optimizing road network designs are important for car commuting reductions. The findings suggest that the optimization of built environment is important for encouraging low-carbon travel.

Key words: traffic engineering, built environment, commuting mode choice, spatial autocorrelation, multilevel Bayesian model

CLC Number:

U491

Chao-ying YIN,Ying LU,Chun-fu SHAO,Jian-xiao MA,De-jie XU. Impacts of built environment on commuting mode choice considering spatial autocorrelation[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 1994-2000.

Figures/Tables 3

Table 1

Fig.1

Table 2

Calibrated results of the models"

特征	变量	模型1		模型2			模型3		模型4
特征	变量	均值	90%置信区间	均值	90%置信区间		均值	90%置信区间	均值	90%置信区间
个体尺度社会经济	年龄	0.012*	（0.005，0.019）	0.034*	（0.011，0.057）	0.037*		（0.021，0.053）	0.028*	（0.021，0.035）
	性别	0.057*	（0.055，0.059）	0.021*	（0.008，0.034）	0.018*		（0.011，0.025）	0.030*	（0.020，0.040）
	教育水平	0.035*	（0.021，0.049）	0.104*	（0.087，0.121）	0.073*		（0.059，0.087）	0.054*	（0.043，0.065）
	户口	0.039*	（0.010，0.068）	0.065*	（0.019，0.111）	0.021*		（0.011，0.031）	0.018*	（0.003，0.032）
	小汽车拥有	0.105*	（0.098，0.112）	0.097*	（0.087，0.107）	0.079*		（0.075，0.083）	0.059*	（0.051，0.067）
	家庭收入1	-0.011*	（-0.020，-0.002）	-0.023*	（-0.028，-0.018）	-0.019*		（-0.025，-0.013）	-0.009*	（-0.015，-0.003）
	家庭收入2	0.043*	（0.012，0.074）	0.028*	（0.012，0.044）	0.039*		（0.030，0.048）	0.074*	（0.060，0.089）
	家庭规模	0.011	（-0.003，0.025）	0.029*	（0.005，0.053）	0.013*		（0.003，0.023）	0.019*	（0.004，0.034）
	家庭学生数	0.009	（-0.014，0.032）	0.017	（-0.003，0.037）	0.019		（-0.014，0.052）	0.023	（-0.008，0.054）
交通小区尺度建成环境	土地利用混合度	-0.015*	（-0.020，-0.010）	-0.021*	（-0.032，-0.010）	-0.013*		（-0.021，-0.005）	-0.018*	（-0.029，-0.007）
	到CBD距离	0.026	（0.015，0.037）	0.043	（0.019，0.067）	0.019		（0.013，0.025）	0.027	（0.014，0.050）
	公共交通站点密度	-0.017*	（-0.024，-0.010）	-0.015*	（-0.024，-0.007）	-0.011*		（-0.014，-0.008）	-0.023*	（-0.034，-0.012）
	交叉口密度	-0.022*	（-0.041，-0.003）	-0.042*	（-0.064，-0.020）	-0.017*		（-0.024，-0.010）	-0.018*	（-0.025，-0.011）
	$σ s$	-	-	5.141*	（4.108，6.174）	11.251*		（9.174，13.328）	15.542*	（12.438，18.646）
DIC		2746.32		2501.16		2515.91			2438.41

Table 2

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特征	变量	变量描述	均值	标准差
个体尺度社会经济	性别	1=男；0=女	0.53	0.50
	年龄	受访者年龄，为连续变量	38.09	10.89
	教育水平	1=拥有本科及以上学位；0=其他	0.32	0.46
	户口	1=本地户口；0=非本地户口	0.92	0.27
	小汽车拥有	1=拥有小汽车；0=不拥有小汽车	0.25	0.44
	家庭收入1	1=收入不高于2万元；0=其他	0.03	0.18
	家庭收入2	1=收入高于10万元；0=其他	0.13	0.34
	家庭规模	同住家庭成员数量	3.00	0.91
	家庭学生数	家庭中学生数量	0.33	0.25
交通小区尺度建成环境	土地利用混合度	基于熵方法得到的土地利用混合程度	0.59	0.07
	到CBD距离	交通小区质心到CBD的距离（单位：km）	4.98	2.89
	公共交通站点密度	单位面积内公共交通站点数目（单位：个/km²）	10.32	5.32
	交叉口密度	单位面积内交叉口数目（单位：个/km²）	30.24	31.29

Impacts of built environment on commuting mode choice considering spatial autocorrelation

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