Mixed Traffic Flow String Stability Analysis for Different CACC Penetration Ranges

Abstract

Abstract:

The string stability of future traffic flow mixed with cooperative adaptive cruise control (CACC) and traditional vehicles will determine the improvement of CACC on traffic capacity and fuel consumptions. Therefore, this paper focuses on string stability of this mixed traffic flow for different CACC penetration ranges. Intelligent driver model (IDM) calibrated using trajectory data and CACC model validated using real vehicles by PATH laboratory of Berkeley are employed to be traditional vehicle car- following model and CACC car- following model respectively. Higher and lower equilibrium velocities are determined based on traditional vehicle stability under small perturbation and numerical simulations are designed considering the randomness of the relative quantity and positions of the two kinds of vehicles. Experimental results show that mixed traffic flow is stable under higher equilibrium velocities for different CACC penetration ranges. However, the mixed traffic flow is unstable under lower equilibrium velocities and lower CACC market penetration. CACC market penetration needs to reach at least 50% to guarantee that the mixed traffic flow can be stable under lower equilibrium velocities.

Key words: traffic engineering, string stability, car-following model, mixed traffic flow, cooperative adaptive cruise control, equilibrium velocity

摘要：

未来协同自适应巡航控制(Cooperative Adaptive Cruise Control，CACC)车辆和传统车辆混合交通流的稳定性决定了CACC技术对交通拥堵、能耗排放的改善程度.鉴于此，研究不同CACC渗透率时这种混合交通流的稳定性.应用基于轨迹数据标定的IDM (Intelligent Driver Model，IDM)模型和由加州伯克利PATH实验室实车测试验证的CACC 模型分别作为传统车辆跟驰模型和CACC车辆跟驰模型.依据传统车辆在扰动下的稳定性，确定高稳态速度和低稳态速度，并考虑两种车型相对数量、相对位置的随机性，设计数值仿真实验.实验结果表明，在高稳态速度下，不同CACC渗透率时混合车队均整体稳定；在低稳态速度下，当CACC渗透率较小时，车队整体不稳定，CACC渗透率需达到50% 以上时，才有可能使得混合车队由不稳定转变为稳定.

关键词: 交通工程, 稳定性, 跟驰模型, 混合交通流, 协同自适应巡航控制, 稳态速度

CLC Number:

U491.112

QIN Yan-yan，WANG Hao，WANGWei，WAN Qian. Mixed Traffic Flow String Stability Analysis for Different CACC Penetration Ranges[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(4): 63-69.

秦严严，王昊，王炜，万千. 不同CACC 渗透率条件下的混合交通流稳定性分析[J]. 交通运输系统工程与信息, 2017, 17(4): 63-69.

Add to citation manager EndNote|Ris|BibTeX

URL: http://www.tseit.org.cn/EN/abstract/abstract19451.shtml

http://www.tseit.org.cn/EN/Y2017/V17/I4/63

References

[1] DEY K C, YAN L, WANG X, et al. A review of communication, driver characteristics, and controls aspects of cooperative adaptive cruise control(CACC)[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(2): 491-509.

[2] HAO WANG, QIAN WAN, WEI WANG. Asymptotic stability analysis of binary heterogeneous traffic based on car-following model[J]. Discrete Dynamics in Nature and Society, vol. 2016, Article ID 3480368, 9 pages, 2016. doi:10.1155/2016/3480368.

[3] SIPAHI R, NICULESCU S I. Stability of car following with human memory effects and automatic headway compensation[J]. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 2010, 368(1928): 4563-4583.

[4] PUEBOOBPAPHAN R, VAN AREM B. Driver and vehicle characteristics and platoon and traffic flow stability: Understanding the relationship for design and assessment of cooperative adaptive cruise control[J]. Transportation Research Record: Journal of the Transportation Research Board, 2010(2189): 89-97.

[5] DAVIS L C. The effects of mechanical response on the dynamics and string stability of a platoon of adaptive cruise control vehicles[J]. Physica A: Statistical Mechanics and its Applications, 2013, 392(17): 3798- 3805.

[6] 杨达, 祝俪菱, 蒲云, 等. 两种驾驶方式构成的异质交通流稳定性研究[J]. 北京理工大学学报, 2013, 33 (11): 1140-1144, 1150. [YAND D, ZHU L L, PU Y, et al. Stability analysis of the heterogeneous traffic flow mixed by two driving styles[J]. Transactions of Beijing Institute of Technology, 2013, 33(11): 1140- 1144, 1150.]

[7] 张德兆, 王建强, 刘佳熙, 等. 加速度连续型自适应巡航控制模式切换策略[J]. 清华大学学报(自然科学版), 2010, 50(8): 1277-1281. [ZHANG D Z, WANG J Q, LIU J X, et al. Switching strategy for adaptive cruise control modes for continuous acceleration[J]. Journal of Tsinghua University(Sci&Tech), 2010, 50(8): 1277- 1281.]

[8] 黄清敏. 车辆纵向跟随稳定性与主动安全控制关键技术研究[D]. 长沙：湖南大学, 2014. [HUANG Q M. Study on the key technology of vehicle longitudinal following stability and active safety control[D]. Changsha: Hunan University, 2014.]

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

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

[11] WANG H, WANG W, CHEN J, et al. Using trajectory data to analyze intradriver heterogeneity in carfollowing[ J]. Transportation Research Record: Journal of the Transportation Research Board, 2010(2188): 85- 95.

[12] PUNZO V, MONTANINO M, CIUFFO B. Do we really need to calibrate all the parameters? variance-based sensitivity analysis to simplify microscopic traffic flow models[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(1): 184-193.

[13] MILANÉS 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] YU S, SHI Z. The effects of vehicular gap changes with memory on traffic flow in cooperative adaptive cruise control strategy[J]. Physica A: Statistical Mechanics And Its Applications, 2015(428): 206-223.

[15] WANG H, WANG W, CHEN J. General Newell model and related second-order expressions[J]. Transportation Research Record: Journal of the Transportation Research Board, 2011(2260): 42-49.

[16] TREIBER M, KESTING A. Traffic flow dynamics: data, models and simulation[M]. Springer-Verlag Berlin Heidelberg, 2013. 104