智能网联环境下高速公路异质交通流建模及仿真

doi:10.13229/j.cnki.jdxbgxb.20240144

摘要/Abstract

摘要：

为探究人工驾驶车辆（HDV）与智能网联车辆（CAV）混合行驶对交通流产生的影响，建立了高速公路异质交通流模型。采用增强型智能驾驶人模型（EIDM）描述HDV跟驰行为，并考虑前方多车的车头间距、速度差、加速度等因素，改进EIDM模型描述CAV跟驰行为；然后利用最小化由换道引起的所有制动（MOBIL）模型描述HDV换道行为，并引入博弈理论，考虑多车竞争与协作关系，构建CAV自主换道与协同换道模型；通过仿真实验评价所建模型合理性，并分析异质交通流运行特征。研究表明：与现有模型相比，本文所建模型在交通流稳定性及行车舒适性等方面具有显著优势；CAV渗透率的提高有利于通行能力的提升，尤其CAV渗透率大于0.4时效果更为显著。

关键词: 交通运输系统工程, 智能交通系统, 异质交通流, 协同换道, 博弈论

Abstract:

To explore the impact of mixed driving for human-driven vehicles （HDV） and Connected and autonomous vehicles （CAV） on traffic flow， a freeway heterogeneous traffic flow model was established. The enhanced intelligent driver model （EIDM） was utilized to describe the car-following behavior of HDV， and the EIDM model was improved to describe the car-following behavior of CAV considering the headway， speed difference and acceleration of multi-vehicles. Then， the minimizing overall braking induced by lane changes （MOBIL） model was utilized to describe the lane-changing behavior of HDV， and the game theory is introduced to consider multi-vehicle competition and cooperation， autonomous and cooperative lane-changing models of CAV were established. Through simulation experiments， the rationality of the model was evaluated and the operation characteristics of heterogeneous traffic flow were analyzed. The research shows that compared with existing models， the model built in this paper demonstrates significant advantages in traffic flow stability and driving comfort. The increase of CAV penetration rate is conducive to the improvement of traffic capacity， especially when the CAV penetration rate is greater than 0.4， the effect is more significant.

Key words: engineering of communications and transportation system, intelligent transportation system, heterogeneous traffic flow, cooperative lane-changing, game theory

中图分类号:

U495

程国柱,陈永胜. 智能网联环境下高速公路异质交通流建模及仿真[J]. 吉林大学学报(工学版), 2025, 55(11): 3583-3592.

Guo-zhu CHENG,Yong-sheng CHEN. Modeling and simulation of freeway heterogeneous traffic flow in connected and autonomous vehicle environment[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(11): 3583-3592.

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参考文献 26

[1]	Shladover S E, Nowakowski C, Lu X Y, et al. Cooperative adaptive cruise control: definitions and operating concepts[J]. Transportation Research Record: Journal of the Transportation Research Board, 2016, 2489(1): 145-152.
[2]	贺正冰. 微观交通模型: 智能网联化转型与通用驾驶人模型框架[J]. 交通运输工程与信息学报, 2022, 20(2): 1-13.
	He Zheng-bing. Microscopic traffic models: transformation in connected environment and generalized driver model[J]. Journal of Transportation Engineering and Information, 2022, 20(2): 1-13.
[3]	Shladover S E, Su D Y, Lu X Y. Impacts of cooperative adaptive cruise control on freeway traffic flow[J]. Transportation Research Record: Journal of the Transportation Research Board, 2012, 2324(1): 63-70.
[4]	Milanes V, Shladover S E. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data[J]. Transportation Research Part C: Emerging Technologies, 2014, 48(1): 285-300.
[5]	Talebpour A, Mahmassani H S. Influence of connected and autonomous vehicles on traffic flow stability and throughput[J]. Transportation Research Part C: Emerging Technologies, 2016, 71(1): 143-163.
[6]	Cui S, Cao F, Yu B, et al. Modeling heterogeneous traffic mixing regular, connected, and connected-autonomous vehicles under connected environment[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 99: 1-19.
[7]	秦严严, 唐鸿辉, 杨金滢, 等. 混有网联车队的道路通行能力分析[J]. 北京交通大学学报, 2022, 46(1): 79-87.
	Qin Yan-yan, Tang Hong-hui, Yang Jin-ying, et al. Analysis on road capacity of connected vehicle platoon on mixed traffic flow[J]. Journal of Beijing Jiaotong University, 2022, 46(1): 79-87.
[8]	秦严严, 胡兴华, 李淑庆, 等. 智能网联环境下混合交通流稳定性解析[J]. 哈尔滨工业大学学报, 2021, 53(3): 152-157.
	Qin Yan-yan, Hu Xing-hua, Li Shu-qing, et al. Stability analysis of mixed traffic flow in connected and autonomous environment[J]. Journal of Harbin Institute of Technology, 2021, 53(3): 152-157.
[9]	马庆禄, 傅宝宇, 曾皓威. 智能网联环境下异质交通流基本图和稳定性分析[J]. 交通信息与安全, 2021, 39(5): 76-84.
	Ma Qing-lu, Fu Bao-yu, Zeng Hao-wei. Fundamental diagram and stability analysis of heterogeneous traffic flow in a connected and autonomous environment[J]. Journal of Transport Information and Safety, 2021, 39(5): 76-84.
[10]	李松, 张开碧, 李永福, 等. 理想诱导环境下的网联车与网联自动驾驶车混合交通流建模研究[J]. 交通运输工程与信息学报, 2023, 21(3): 31-58.
	Li Song, Zhang Kai-bi, Li Yong-fu, et al. Modeling a mixed traffic flow of connected vehicles and connected autonomous vehicles in an ideal induction environment[J]. Journal of Transportation Engineering and Information, 2023, 21(3): 31-58.
[11]	Yu Y, Liu S, Jin P J, et al. Multi-player dynamic game-based automatic lane-changing decision model under mixed autonomous vehicle and human-driven vehicle environment[J]. Transportation Research Record Journal of the Transportation Research Board, 2020, 2674(4): 1-19.
[12]	曲大义, 黑凯先, 郭海兵, 等. 车联网环境下车辆换道博弈行为及模型[J]. 吉林大学学报: 工学版, 2022, 52(1): 101-109.
	Qu Da-yi, Kai-xian Hei, Guo Hai-bing, et al. Game behavior and model of lane-changing on the internet of vehicles environment[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(1): 101-109.
[13]	吴德华, 彭锐, 陈荣峰. 异质流网联车的不同换道集聚策略[J]. 西南交通大学学报, 2023, 58(2): 348-356.
	Wu De-hua, Peng Rui, Chen Rong-feng. Hybrid characteristics of heterogeneous traffic flow under different aggregating lane-change strategies in intelligent network[J]. Journal of Southwest Jiaotong University, 2023, 58(2): 348-356.
[14]	潘义勇, 王松. 网联自动驾驶环境下改进的加权MOBIL自主性换道决策模型[J]. 重庆交通大学学报: 自然科学版, 2021, 40(5): 46-52.
	Pan Yi-yong, Wang Song. Improved weighted MOBIL decision model for autonomous lane change in networked autopilot environment[J]. Journal of Chongqing Jiaotong University (Natural Science), 2021, 40(5): 46-52.
[15]	孙曼曼, 陈珍萍, 李海峰, 等. 基于博弈论的网联自动驾驶车辆协同换道研究[J]. 计算机仿真, 2023, 40(1): 161-166.
	Sun Man-man, Chen Zhen-ping, Li Hai-feng, et al. Research on cooperative lane change of networked autonomous vehicles based on game theory[J]. Computer Simulation, 2023, 40(1): 161-166.
[16]	Ye L, Yamamoto T. Modeling connected and autonomous vehicles in heterogeneous traffic flow[J]. Physica A: Statistical Mechanics and its Applications, 2018, 490(1): 269-277.
[17]	Zhou Y J, Zhu H B, Guo M M, et al. Impact of CACC vehicles cooperative driving strategy on mixed four-lane highway traffic flow[J]. Physica A: Statistical Mechanics and its Applications, 2019, 540(1): 1-25.
[18]	郭静秋, 方守恩, 曲小波, 等. 基于强化协作博弈方法的双车道混合交通流特性[J]. 同济大学学报: 自然科学版, 2019, 47(7): 976-983.
	Guo Jing-qiu, Fang Shou-en, Qu Xiao-bo, et al. Characteristics of mixed traffic flow in two-lane scenario based on cooperative gaming method[J]. Journal of Tongji University (Natural Science), 2019, 47(7): 976-983.
[19]	梁军, 耿浩然, 陈龙, 等. 融入公交车与自动驾驶车队的异质交通流模型[J]. 西南交通大学学报, 2023, 58(5): 1090-1099.
	Liang Jun, Geng Hao-ran, Chen Long, et al. Integrated heterogeneous traffic flow model of bus and autonomous vehicle platoon[J]. Journal of Southwest Jiaotong University, 2023, 58(5): 1090-1099.
[20]	单肖年, 万长薪, 李志斌, 等. 智能网联环境下多车道异质交通流建模与仿真[J]. 交通运输系统工程与信息, 2022, 22(6): 74-84.
	Shan Xiao-nian, Wan Chang-xin, Li Zhi-bin, et al. Modeling and simulation of multi-lane heterogeneous traffic flow in intelligent and connected vehicle environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 74-84.
[21]	Treiber M, Hennecke A, Helbing D. Congested traffic states in empirical observations and microscopic simulations[J]. Physical Review E, 2000, 62(2): 1805-1824.
[22]	Kesting A, Treiber M, Helbing D. Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity[J]. Philosophical Transactions of the Royal Society A, 2010, 368(1928): 4585-4605.
[23]	Guériau M, Billot R, Faouzin E E, et al. How to assess the benefits of connected vehicles? A simulation framework for the design of cooperative traffic management strategies[J]. Transportation Research Part C: Emerging Technologies, 2016, 67(1): 266-279.
[24]	Kesting A, Treiber M, Helbing D. General lane-changing model MOBIL for car-following model[J]. Transportation Research Record, 2007, 1999(1): 86-94.
[25]	Nie J, Zhang J, Ding W, et al. Decentralized cooperative lane-changing decision-making for connected autonomous vehicles[J]. IEEE Access, 2017, 4(1): 9413-9420.
[26]	Gurupackiam S, Jones S L. Empirical study of lane changing in urban streets under varying traffic conditions[J]. Procedia-Social and Behavioral Sciences, 2011, 16: 259-269.

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		原前车
		换道	不换道
目标车辆	换道	u_TV，11， u_LV，11	u_TV，12， u_LV，12
目标车辆	不换道	u_TV，21， u_LV，21	u_TV，22， u_LV，22

		邻前车
		换道	不换道
目标车辆	换道	u_TV，11， u_TLV，11	u_TV，12， u_TLV，12
目标车辆	不换道	u_TV，21， u_TLV，21	u_TV，22， u_TLV，22

		原前车
		换道	不换道	换道	不换道
		邻前车换道		邻前车不换道
目标车辆	换道	u_TV，111， u_LV，111， u_TLV，111	u_TV，121， u_LV，121， u_TLV，121	u_TV，112， u_LV，112， u_TLV，112	u_TV，122， u_LV，122， u_TLV，122
目标车辆	不换道	u_TV，211， u_LV，211， u_TLV，211	u_TV，221， u_LV，221， u_TLV，221	u_TV，212， u_LV，212， u_TLV，212	u_TV，222， u_LV，222， u_TLV，222

组合模型	跟驰模型		换道模型
组合模型	HDV	CAV	HDV	CAV
现有模型^［20］	IDM模型	IDM模型+CACC模型	STCA模型	MOBIL模型
组合模型1	IDM模型^［23］	CIDM模型^［25］	MOBIL模型^［26］	CMOBIL模型^［27］
组合模型2	EIDM模型^［24］	CEIDM模型	MOBIL模型	CMOBIL模型
组合模型3	EIDM模型	CEIDM模型	MOBIL模型	CMOBIL自主换道模型+GT协同换道模型

参数	小客车		大货车
参数	HDV	CAV	HDV	CAV
T/s	1.5	1.1，0.6	2.0	1.1，0.6
长，宽/m，m	5，1.8	5，1.8	10，2.0	10，2.0
v_f/（km·h^-1）	120	120	85	85
s₀/m	2	2	4	4
a/（m·s^-2）	1.4	1.4	0.7	0.7
b/（m·s^-2）	2	2	2	2
δ	4	4	4	4
Δa/（m·s^-2）	0.3	0.3	0.3	0.3
ζ	0.10	0.10	0.10	0.10
b_safe/（m·s^-2）	-3	-3	-3	-3