Eco-car-following strategy based on the CO2 emission characteristics of car-following pairs

doi:10.3969/j.issn.1674-8484.2025.04.008

Abstract

Abstract:

An eco-car-following (ECF) strategies was explored with the CO₂ emissions of car-following behavior in mixed traffic flow under the environment of intelligent connected vehicles. The vehicle trajectory data was used to extract multi-dimensional car-following behavior feature parameters. An eXtreme Gradient Boosting (XGBoost) model was established with calculating and analyzing the effects of car-following behavior feature parameters on CO₂ emissions during the car-following process by using the Shapley Additive exPlanations (SHAP) algorithm. The intelligent driver model of human-driven vehicles was calibrated. The Simulation of Ur-ban MObility (SUMO) platform was using to simulate 11 mixed traffic scenarios. The Adaptive Cruise Control (ACC) and the Cooperative Adaptive Cruise Control (CACC) models were employed for Connected and Automated Vehicles (CAVs). The results show that the instantaneous mass CO₂ emissions of CACC-CACC vehicle pairs de-crease by more than 60% when the proportion of CACC vehicles exceeds 50%. There-fore, the strategy reduces CO₂ emissions for CAVs and CACC-CACC car-following pairs in mixed traffic flow scenarios.

Key words: connected automated vehicles (CAVs), mixed traffic flow, eco-car-following (ECF) strategy, car-following behavior, simulation of urban mobility (SUMO) platform

CLC Number:

U491

YU Qian, GUO Yuanyuan, YANG Mingpeng, ZHANG Yuting. Eco-car-following strategy based on the CO₂ emission characteristics of car-following pairs[J]. Journal of Automotive Safety and Energy, 2025, 16(4): 577-586.

Figures/Tables 10

References 19

[1]	李国法, 陈耀昱, 吕辰, 等. 智能汽车决策中的驾驶行为语义解析关键技术[J]. 汽车安全与节能学报, 2019, 10(4): 391-412.
	LI Guofa, CHEN Yaoyu, LÜ Chen, et al. Key techniques of semantic analysis of driving behavior in decision making of autonomous vehicles[J]. J Autom Safe Energ, 2019, 10(4): 391-412. (in Chinese)
[2]	张铎, 饶红玉, 刘佳琦, 等. 基于自然驾驶数据的驾驶人跟驰行为内在异质性预测与建模[J]. 交通运输系统工程与信息, 2023, 23(5): 33-44.
	ZHANG Duo, RAO Hongyu, LIU Jiaqi, et al. Intra-driver heterogeneity prediction and modeling based on naturalistic driving experiment[J]. J Transp Syst Eng Info Techn, 2023, 23(5): 33-44. (in Chinese)
[3]	冉斌, 谭华春, 张健, 等. 智能网联交通技术发展现状及趋势[J]. 汽车安全与节能学报, 2018, 9(2): 119-130.
	RAN Bin, TAN Huachun, ZHANG Jian, et al. Development status and trend of connected automated vehicle highway system[J]. J Autom Safe Energ, 2018, 9(2): 119-130. (in Chinese)
[4]	杨秀建, 黄菁菁, 王曦. 考虑CACC汽车队列动态特性的混合交通流建模与仿真[J]. 系统仿真学报, 2024, 24(175): 1-13.
	YANG Xiujian, HUANG Jingjing, WANG Xi. Modeling and simulation of hybrid traffic flow considering the inherent dynamics of CACC vehicular platoons[J]. J Syst Simul, 2024, 24(175): 1-13. (in Chinese)
[5]	LI Renjie, QIN Yanyan. Car-following strategy involving stabilizing traffic flow with connected automated vehicles to reduce particulate matter (PM) emissions in rainy weather[J]. Sustain, 2024, 16(5): 2045.
[6]	HUANG Yan, YAN Xuedong, LI Xiaomeng, et al. Evaluating the safe and eco-driving performances of car-following behaviors in a vehicle platoon under foggy conditions[J]. J Transp Saf Secur, 2024, 16(2): 204-229.
[7]	QIN Yanyan, XIAO Tengfei, HE Zhengbing. Emissions-reduction strategy for connected autonomous vehicles on mixed traffic freeways[J]. Phys A, 2024, 653: No 130113.
[8]	HU Xiaosong, ZHANG Xiaoqian, TANG Xiaolin, et al. Model predictive control of hybrid electric vehicles for fuel economy, emission reductions, and inter-vehicle safety in car-following scenarios[J]. Energy, 2020, 196: Paper No 117101.
[9]	李传耀, 张帆, 王涛, 等. 基于深度强化学习的道路交叉口生态驾驶策略研究[J]. 交通运输系统工程与信息, 2024, 24(01): 81-92.
	LI Chuanyao, ZHANG Fan, WANG Tao, et al. Signalized Intersection Eco-driving Strategy Based on Deep Reinforcement Learning[J]. J Transp Syst Eng Info Techn, 2024, 24(01): 81-92. (in Chinese)
[10]	DONG Haoxuan, ZHUANG Weichao, CHEN Boli, et al. A comparative study of energy-efficient driving strategy for connected internal combustion engine and electric vehicles at signalized intersections[J]. Appl Energy, 2022, 310: No 118524.
[11]	秦严严, 王昊, 冉斌. CACC车辆跟驰建模及混合交通流分析[J]. 交通运输系统工程与信息, 2018, 18(2): 60-65.
	QIN Yanyan, WANG Hao, RAN Bin. Car-following modeling for CACC vehicles and mixed traffic flow analysis[J]. J Transp Syst Eng Info Techn, 2018, 18(2): 60-65. (in Chinese)
[12]	Federal Highway Administration. US highway 101 dataset[Z/OL]. (2007-01-01). https://www.fhwa.dot.gov.
[13]	Chong L S, Abbas M M, Flintsch A M, et al. A rule-based neural network approach to model driver naturalistic behavior in traffic[J]. Transp Res Pt C-Emerg Technol, 2013, 32: 207-223.
[14]	XU Wei, LI Nan, LIANG Wenli, et al. Dissipation characteristics of vehicle queue in V2X environment based on improved car-following model[J]. Math Probl Eng, 2022, 2022: 1-17.
[15]	王雪松, 朱美新. 基于自然驾驶数据的中国驾驶人城市快速路跟驰模型标定与验证[J]. 中国公路学报, 2018, 31(9): 129-137.
	WANG Xuesong, ZHU Meixin. Calibrating and validating car-following models on urban expressways for chinese drivers using naturalistic driving data[J]. China J Highw Transp, 2018, 31(9): 129-137. (in Chinese)
[16]	Lundberg S, Lee S I. Welcome to the SHAP documentation[Z/OL]. (2025-03-05). https://shap.readthedocs.io/en/latest/.
[17]	Treiber M, Hennecke A, Helbing D. Congested traffic states in empirical observations and microscopic simulations[J]. Phys Rev E, 2000, 62(2): 1805-1824.
[18]	曹金亮, 史忠科, 房雅灵. 基于城市主干路交通流数据的跟驰模型标定[J]. 交通信息与安全, 2014, 32(6): 82-88.
	CAO Jinliang, SHI Zhongke, FANG Yaling. Calibrating car-following models based on urban arterial video data[J]. J Transp Info Saf, 2014, 32(6): 82-88. (in Chinese)
[19]	秦严严, 杨晓庆, 王昊. 智能网联混合交通流CO₂排放影响及改善方法[J]. 吉林大学学报:工学版, 2023, 53(1): 150-158.
	QIN Yanyan, YANG Xiaoqing, WANG Hao. Impacts of CO₂ emissions and improving method for connected and automated mixed traffic flow[J]. J Jilin Univ: Eng Techn Ed, 2023, 53(1): 150-158. (in Chinese)

标定参数	默认值	标定范围	标定结果
v₀(t) / (m·s^-1)	不设置上限	[15, 40]	17.61
T₀(t) / s	0.25	[0.1, 5]	0.10
S_jam / m	2.5	[0.1, 10]	0.10
a_max / (m·s^-2)	2.6	[0.1, 5]	0.39
a_comf / (m·s^-2)	4.5	[0.1, 5]	2.84
β	4	20	20

标定参数	默认值	标定范围	标定结果
v₀(t) / (m·s^-1)	不设置上限	[15, 40]	17.61
T₀(t) / s	0.25	[0.1, 5]	0.10
S_jam / m	2.5	[0.1, 10]	0.10
a_max / (m·s^-2)	2.6	[0.1, 5]	0.39
a_comf / (m·s^-2)	4.5	[0.1, 5]	2.84
β	4	20	20

参数	跟驰模型
参数	HV	CACC	ACC
颜色	红色	绿色	蓝色
车辆长度 / m	5	5	5
车辆加速能力 / (m·s^-2)	0.39	2.6	2.6
车辆减速能力 / (m·s^-2)	2.84	4.5	4.5
车辆最大速度 / (m·s^-1)	17.61	34	34
车辆与前车距离 / m	0.10	2.5	2.5
加速度指数	20	-	-
车头时距 / s	0.10	1/2*	1/2*
车辆出发速度 / (m·s^-1)	随机	随机	随机
驾驶员完美程度	0.5	0.5/0*	0.5/0*

参数	跟驰模型
参数	HV	CACC	ACC
颜色	红色	绿色	蓝色
车辆长度 / m	5	5	5
车辆加速能力 / (m·s^-2)	0.39	2.6	2.6
车辆减速能力 / (m·s^-2)	2.84	4.5	4.5
车辆最大速度 / (m·s^-1)	17.61	34	34
车辆与前车距离 / m	0.10	2.5	2.5
加速度指数	20	-	-
车头时距 / s	0.10	1/2*	1/2*
车辆出发速度 / (m·s^-1)	随机	随机	随机
驾驶员完美程度	0.5	0.5/0*	0.5/0*

场景	CAV渗透率 / %	交通量/ (辆·h^-1)	1 h 交通量 / 辆			CACC/ACC 车辆比例
场景	CAV渗透率 / %	交通量/ (辆·h^-1)	HV车辆	CACC车辆	ACC车辆	CACC/ACC 车辆比例
1*	50	1 600	800	400	400	1∶1
2	25	1 600	1 200	200	200	1∶1
3	75	1 600	400	600	600	1∶1
4	100	1 600	0	800	800	1∶1
5	50	800	400	200	200	1∶1
6	50	2 400	1 200	600	600	1∶1
7	50	3 200	1 600	800	800	1∶1
8	50	1 600	800	0	800	0∶800
9	50	1 600	800	200	600	1∶3
10	50	1 600	800	600	200	3∶1
11	50	1 600	800	800	0	800∶0

Eco-car-following strategy based on the CO₂ emission characteristics of car-following pairs

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CAV渗透率 %	CO₂瞬时质量排放 / (g·s^-1)		减少比例 %
CAV渗透率 %	不使用ECF策略	使用ECF策略	减少比例 %
50	21.30	6.72	68.48
75	19.48	6.89	64.63
100	17.87	6.95	61.11
交通量 / (辆 · h^-1)
1 600	21.30	6.72	68.48
2 400	7.71	6.72	12.92
3 200	7.93	6.63	16.34
CACC比例 / %
50	21.30	6.72	68.48
75	19.00	6.94	63.50
100	17.51	6.94	60.40

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