城市建成环境与道路交通运行关系

doi:10.13229/j.cnki.jdxbgxb.20230337

Abstract

Abstract:

This study provides a quantitative analysis of the relationship between the built environment and road traffic performance. We define traffic performance by measuring average travel speeds on road segments and develop a random forest model using multi-source panel data to predict speeds based on built environment variables. The results demonstrate high predictive accuracy， revealing a significant correlation between road traffic performance and the built environment， characterized by complex non-linear relationships. Our work addresses three limitations in prior research： overlooking dynamic traffic performance characteristics； potential chance bias in small-area studies； and insufficient exploration of non-linear relationships. These findings provide actionable insights for urban planners and policymakers to optimize transport infrastructure and management strategies.

Key words: urban traffic, correlation mechanism, random forest, built environment, travel speed, non-linear relationship

CLC Number:

U121

Guang-yue NIAN,Hai-xiao PAN,Jian SUN. Exploring relationship between urban built environment and road traffic performance[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(1): 141-149.

Figures/Tables 7

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变量名称	符号	最小值	中值	平均值	最大值	标准差
路段长度	l	250.288	602.351	665.739	2 202.920	300.180
POI混合度	m	0	0.864	0.785	1	0.255
是否有地铁站	s	0	0	0.167	1	0.373
车辆服务密度	d₁	0	0.021	0.061	4.744	0.164
住宅小区密度	d₂	0	0.047	0.092	0.667	0.111
医疗卫生服务密度	d₃	0	0.034	0.127	1.802	0.213
停车场出入口密度	d₄	0	0	0.058	0.955	0.114
公交站密度	d₅	0	0.033	0.040	0.230	0.035
科技文化服务密度	d₆	0	0.036	0.114	2.695	0.205
生活服务密度	d₇	0	0.084	0.299	9.017	0.593
体育休闲服务密度	d₈	0	0.027	0.109	3.773	0.220
政府机构密度	d₉	0	0.039	0.127	1.963	0.224
购物服务密度	d₁₀	0	0.122	0.630	22.873	1.434
餐饮服务密度	d₁₁	0	0.065	0.285	7.512	0.566
公司企业密度	d₁₂	0	0.052	0.117	3.079	0.213
金融保险密度	d₁₃	0	0	0.060	1.308	0.126
住宿服务密度	d₁₄	0	0	0.076	3.282	0.215
商住楼密度	d₁₅	0	0	0.021	0.532	0.057
景点密度	d₁₆	0	0	0.019	0.863	0.052

建成环境密度变量	符号	因子变量
建成环境密度变量	符号	f₁	f₂	f₃	f₄	f₅
车辆服务密度	d₁	0.195	0.746	0.195	0.270	0.040
住宅小区密度	d₂	0.244	0.341	0.794	-0.021	0.110
医疗卫生服务密度	d₃	0.861	0.299	0.206	0.208	0.064
停车场出入口密度	d₄	0.667	0.226	0.487	0.153	0.087
公交站密度	d₅	0.746	0.302	0.368	0.197	0.037
科技文化服务密度	d₆	0.540	0.544	0.302	0.189	0.024
生活服务密度	d₇	0.709	0.130	0.356	0.393	0.036
体育休闲服务密度	d₈	0.887	0.323	0.185	0.044	0.071
政府机构密度	d₉	0.645	0.643	0.116	0.008	0.061
购物服务密度	d₁₀	0.534	0.067	0.621	0.317	-0.003
餐饮服务密度	d₁₁	0.101	0.103	0.077	-0.016	0.984
公司企业密度	d₁₂	0.468	0.725	0.190	-0.064	0.162
金融保险密度	d₁₃	0.786	0.381	0.111	0.062	0.063
住宿服务密度	d₁₄	0.842	0.250	0.186	0.122	0.042
商住楼密度	d₁₅	0.864	0.210	0.227	0.049	0.106
景点密度	d₁₆	0.164	0.135	0.072	0.927	-0.021

变量	Estimate	Std. Error	t value	P_r（>\|t\|）
模型评价	R²	0.011	p-value	3.6×10^-9
截距	23.958	0.943	25.409	<2.0×10^-16***
是否有地铁站s	-4.157	0.655	-6.347	2.4×10^-10***
POI混合度m	1.360	0.960	1.416	0.157
路段长度l	0.003	0.001	3.819	0.000 1***
因子f₁	0.221	0.237	0.934	0.350
因子f₂	0.213	0.241	0.884	0.377
因子f₃	0.241	0.242	0.998	0.318
因子f₄	0.157	0.253	0.622	0.534
因子f₅	0.033	0.339	0.097	0.923

Exploring relationship between urban built environment and road traffic performance

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