Study on the Impact Characteristics of Horizontal Curve Elements on Carbon Emissions from Passenger Car Operation

doi:10.12141/j.issn.1000-565X.240047

Abstract

Abstract:

China’s “14th Five-Year Plan” places higher demands on green transportation development, with emissions from traffic operations being the primary source of carbon emissions in the transportation sector. To investigate the factors influencing carbon emissions of passenger cars on highway curved segments, this study conducted on-site driving tests using OBD-equipped vehicles to collect driving data from typical curved road segments in Guangdong Province, and obtains carbon emission data through the IPCC carbon emission accounting method. Re-levant evaluation indicators influencing passenger car emissions were selected based on road alignment, and gray relational analysis was used to calculate the correlations between these indicators. The results indicate that among the geometric alignment elements of horizontal curve sections, indicators such as the proportion of transition curve length and transition curve parameters are significantly correlated with the segmental carbon emission rate. The radius of the circular curve is also significantly correlated within a specific range. For non-geometric factors, indicators such as the standard deviation and mean of acceleration show significant correlations with carbon emissions, and these two indicators are further associated with geometric factors like transition curve parameters and the proportion of transition curve length. Based on the results of the grey relational analysis, eight correlated indicators were selected, and a grey GM(1, N) model was developed to predict the total carbon emissions of passenger cars on horizontal curve sections. The prediction results show an average relative error of 5.10% compared to the actual values. The predictive performance of the model surpasses that of traditional multiple regression models, demonstrating outstanding performance and reliability in scenarios with limited data.The findings of this study can identify key design and operational parameters significantly influencing carbon emissions, providing a theoretical basis for the low-carbon optimization and management of highway horizontal curve sections.

Key words: highway, horizontal curve, carbon emission rate, gray relational analysis, passenger car

CLC Number:

WANG Xiaofei, LUO Zhen, WANG Shaohua, PAN Ling, ZENG Qiang. Study on the Impact Characteristics of Horizontal Curve Elements on Carbon Emissions from Passenger Car Operation[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(2): 68-79.

Figures/Tables 17

Table 1

Fig.1

Table 2

Table 3

Each evaluation index and the symbol"

线形评价指标	符号		符号
前缓和曲线参数/m	A₁	平均速度/（km·h^-1）	$v ¯$
后缓和曲线参数/m	A₂	速度标准差/（km·h^-1）	$σ v$
前缓和曲线占比	I₁	平均加速度/（m·s²）	$a ¯$
后缓和曲线占比	I₂	加速度标准差/（m·s²）	$σ a$
圆曲线半径/m	R	加速阶段加速度平均值/（m·s²）	a⁺
圆曲线长度占比	I_r	减速度平均值/（m·s²）	a^-
平曲线转角/（°）	γ	平曲线路段碳排放率/（g·km^-1）	E

Table 3

Table 4

Gray relational analysis initial data table"

路段标号	A₁/m	A₂/m	I₁	I₂	R/m	I_r	γ/（°）	$v ¯$ / （km·h^-1）	$σ v$ / （km·h^-1）	$a ¯$ / （m·s^-2）	$σ a$ / （m·s^-2）	$a +$ / （m·s^-2）	$a -$ / （m·s^-2）	E/ （g·km^-1）
1	174.64	174.64	0.13	0.13	610	0.74	26	45.76	3.93	0.18	0.53	0.502	-0.43	88.61
2	143.18	143.18	0.13	0.13	410	0.74	40	58.84	4.46	0.13	0.52	0.320	-0.49	79.65
︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙
24	142.83	142.83	0.27	0.27	340	0.45	16	23.44	2.81	0.22	0.35	0.190	0.00	236.81
25	148.32	148.32	0.37	0.37	440	0.26	4	59.45	1.93	0.08	0.29	0.000	-0.22	52.91
26	45.95	45.95	0.32	0.32	143	0.37	17	51.26	0.75	0.14	0.15	0.000	-0.28	91.13

Table 4

Table 5

Table 6

Euclidean grey correlation degree weighting factors of non-geometric indicators"

指标	权重因子w_i		权重因子w_i
$v ¯$	0.18	$σ a$	0.18
$σ v$	0.20	a $+$	0.20
$a ¯$	0.19	a $-$	0.05

Table 6

Table 7

Table 8

Correlation degree between non-geometric indicators and carbon emission rate"

指标	灰色关联度		模糊隶属度		指标	灰色关联度		模糊隶属度
指标	邓式	欧式	模糊隶属度		指标	邓式	欧式	模糊隶属度
$v ¯$	0.649 5	0.639 0	0.782 4	$σ a$		0.737 1	0.701 9	0.934 8
$σ v$	0.612 1	0.567 8	0.823 2	a $+$		0.710 2	0.650 8	0.926 1
$a ¯$	0.632 9	0.584 4	0.777 7	a $-$		0.690 5	0.647 7	0.911 9

Table 8

Fig.2

Fig.3

Fig.4

Fig.5

Table 9

Fig.6

Table 10

Table 11

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序号	平曲线^1）要素	范围	平均值
1	平曲线长度L/m	［105.74，406.71］	219.07
2	缓和曲线长度L_S/m	［0，136.51］	72.44
3	圆曲线长度L_R/m	［35.22，352.25］	143.34
4	圆曲线半径R/m	［143.32，12 000］	2 725.10
5	前缓和曲线参数A₁/m	［0，259.81］	100.22
6	后缓和曲线参数A₂/m	［0，259.81］	100.21
7	转角γ/（°）	［2.17，56.73］	18.96

指标	灰色关联度		模糊隶属度		指标	灰色关联度		模糊隶属度
指标	邓式	欧式	模糊隶属度		指标	邓式	欧式	模糊隶属度
A₁	0.706 1	0.828 8	0.725 7	R		0.629 8	0.327 5	0.197 6
A₂	0.709 6	0.831 7	0.734 9	I_r		0.719 8	0.829 6	0.744 5
I₁	0.707 9	0.799 0	0.730 5	γ		0.608 2	0.673 8	0.682 9
I₂	0.727 5	0.807 4	0.755 1

指标	灰色关联度		模糊隶属度		指标	灰色关联度		模糊隶属度
指标	邓式	欧式	模糊隶属度		指标	邓式	欧式	模糊隶属度
A₁	0.722 5	0.825 1	0.763 3	R		0.642 5	0.340 7	0.230 4
A₂	0.727 1	0.828 7	0.781 9	I_r		0.666 6	0.805 9	0.723 0
I₁	0.678 1	0.784 5	0.646 1	γ		0.634 1	0.687 8	0.710 5
I₂	0.691 6	0.793 4	0.698 2

试验	实际值/g	预测值/g	误差/g	相对误差/%
Trip1	627.54	652.51	24.97	3.98
Trip2	558.98	588.19	29.21	5.23
Trip3	643.99	678.93	34.93	5.43
Trip4	499.38	547.92	48.53	9.72
Trip5	604.01	592.02	11.99	1.98
Trip6	553.45	577.30	23.85	4.31

模型	MAD	RMSE	MSE
灰色GM（1，N）模型	5.01	6.33	40.13
多元线性回归模型	31.19	40.16	1 613.45