智能网联环境下复杂异质交通流稳定性解析

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (6): 114-120.

• 智能交通系统与信息技术 • 上一篇下一篇

智能网联环境下复杂异质交通流稳定性解析

李霞^*，汪一戈，崔洪军，朱敏清，王欣桐

河北工业大学土木与交通学院，天津 300401

收稿日期:2020-08-21 修回日期:2020-09-17 出版日期:2020-12-25 发布日期:2020-12-25
作者简介:李霞(1981-)，女，河北新乐人，副教授，博士.
基金资助:
国家自然科学基金青年科学基金/Young Scientists Fund of the National Natural Science Foundation of China (51908187)；国家自然科学基金/National Natural Science Foundation of China(51678212)；河北省自然科学基金/Natural Science Foundation of Hebei Province, China(E2019202449).

Stability Analysis of Complex Heterogeneous Traffic Flow under Connected and Autonomous Environment

LI Xia, WANG Yi-ge, CUI Hong-jun, ZHU Min-qing, WANG Xin-tong

School of Civil Engineering and Transportation, Hebei University of Technology, Tianjin 300401, China

Received:2020-08-21 Revised:2020-09-17 Online:2020-12-25 Published:2020-12-25

摘要/Abstract

摘要：

针对网联车辆与普通车辆构成的异质交通流，考虑网联车辆车对车(V2V)、车对基础设施(V2I)不同通信技术，研究复杂异质交通流稳定性.基于李雅普诺夫理论，分析对比不同通信技术下网联车辆与普通车辆构成的异质交通流稳定性；基于V2V、V2V/V2I的网联车辆和普通车辆以不同比例混合时，进行异质交通流稳定性判别及稳定域解析.通过数值仿真，验证理论解析的正确性.研究结果表明，基于V2V/V2I的网联车辆比基于V2V的网联车辆混入对异质交通流稳定性改善效果更显著.基于V2V、V2V/V2I的网联车辆同时汇入普通车辆交通流的情况下，当基于V2V的网联车辆比例较低时，不会明显提高异质交通流稳定性；而基于 V2V/V2I的网联车辆即使在低比例时，也会显著改善交通流稳定性；且基于V2V网联车辆比例较低时，平衡态临界速度值随着基于V2V/V2I网联车辆的比例增加近似呈线性减小.

关键词: 交通工程, 稳定性解析, 异质交通流, 网联车辆, V2V/V2I, 跟驰模型

Abstract:

Considering the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies of connected vehicles, the stability of the complex heterogeneous traffic flow composed of connected vehicles and regular vehicles was studied. Based on the Lyapunov theory, the stability of heterogeneous traffic flow under different communication technologies was compared. Then the stability conditions and stability region of heterogeneous traffic flow were analyzed when V2V- based connected vehicles, V2V/V2I- based connected vehicles, and regular vehicles are mixed with different proportion rates. The numerical simulations were carried out to verify the correctness of the theoretical analysis. The results show that the heterogeneous traffic flow mixed with V2V/V2I- based connected vehicles has a better stability effect than V2V- based vehicles. In the case of the V2V- based and V2V/V2I- based connected vehicles merge into traffic flow composed of regular vehicles, low penetration rates of V2V- based connected vehicles do not result in significant stability improvements, whereas stability is improved even at low penetration rates of V2V/V2I- based connected vehicles. In addition, when the penetration rates of V2V- based connected vehicles are low, the critical velocity of equilibrium increases approximately linearly as the proportion rates of V2V/V2I-based connected vehicles increases.

Key words: traffic engineering, stability analysis, heterogeneous traffic flow, connected vehicles, V2V/V2I, car following model

中图分类号:

U491

李霞，汪一戈，崔洪军，朱敏清，王欣桐. 智能网联环境下复杂异质交通流稳定性解析[J]. 交通运输系统工程与信息, 2020, 20(6): 114-120.

LI Xia, WANG Yi-ge, CUI Hong-jun, ZHU Min-qing, WANG Xin-tong. Stability Analysis of Complex Heterogeneous Traffic Flow under Connected and Autonomous Environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(6): 114-120.

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参考文献

[1] 王昊, 秦严严. 网联车混合交通流渐进稳定性解析方法 [J]. 哈尔滨工业大学学报, 2019, 51(3): 88- 91. [WANG H, QIN Y Y. Asymptotic stability analysis of traffic flow mixed with connected vehicles[J]. Journal of Harbin Institute of Technology, 2019, 51(3): 88-91.]

[2] CHEN S, HU J, SHI Y, et al. Vehicle- to- everything (v2x) services supported by LTE-based systems and 5G [J]. IEEE Communications Standards Magazine, 2017, 1(2): 70-76.

[3] BOSE A, IOANNOU P A. Analysis of traffic flow with mixed manual and semi-automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2003, 4(4): 173-188.

[4] XIE D F, ZHAO X M, HE Z. Heterogeneous traffic mixing regular and connected vehicles: modeling and stabilization[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(6): 2060-2071.

[5] CHANG X, LI H, RONG J, et al. Analysis on traffic stability and capacity for mixed traffic flow with platoons of intelligent connected vehicles[J]. Physica A: Statistical Mechanics and its Applications, 2020, 557.

[6] LI P Y, SHRIVASTAVA A. Traffic flow stability induced by constant time headway policy for adaptive cruise control vehicles[J]. Transportation Research Part C, 2002, 10(4): 275-301.

[7] LI Z, LI W, XU S, et al. Stability analysis of an extended intelligent driver model and its simulations under open boundary condition[J]. Physica A: Statistical Mechanics and its Applications, 2015, 419: 526-536.

[8] 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): 143-163.

[9] 秦严严, 王昊, 王炜, 等. 混有协同自适应巡航控制车辆的异质交通流稳定性解析与基本图模型[J]. 物理学报, 2017, 66(9): 257-265. [QIN Y Y, WANG H, WANG W, et al. Stability analysis and fundamental diagram of heterogeneous traffic flow mixed with cooperative adaptive cruise control vehicles[J]. Acta Physica Sinica, 2017, 66(9): 257-265.]

[10] 蒋阳升, 胡蓉, 姚志洪, 等. 智能网联车环境下异质交通流稳定性及安全性分析[J]. 北京交通大学学报, 2020, 44(1): 27-33. [JIANG Y S, HU R, YAO Z H, et al. Stability and safety analysis for heterogeneous traffic flow composed of intelligent and connected vehicles[J]. Journal of Beijing Jiaotong University, 2020, 44(1): 27-33.]

[11] BANDO M, HASEBE K, NAKAYAMA A, et al. Dynamical model of traffic congestion and numerical simulation[J]. Physical Review E, 1995, 51(2): 1035- 1042.

[12] WANG H, WANG W, CHEN J, et al. Can we trust the speed- spacing relationship estimated by car- following model from non- stationary trajectory data?[J]. Transportmetrica A: Transport Science, 2019, 15(2): 263-284.

[13] 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: 285-300.

[14] TREIBER M, HENNECKE A, HELBING D. Congested traffic states in empirical observations and microscopic simulations[J]. Physical Review E, 2000, 62(2): 1805- 1824.

[15] WARD J A. Heterogeneity, lane- changing and instability in traffic: a mathematical approach[D]. Bristol: University of Bristol, 2009.

智能网联环境下复杂异质交通流稳定性解析

Stability Analysis of Complex Heterogeneous Traffic Flow under Connected and Autonomous Environment

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