An Impact Analysis of the Proportion of Adaptive Cruise Control Vehicles on the Safety of Mixed Traffic Flow at the Off-ramp Diverging Area
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摘要: 在人工驾驶车辆、自适应巡航控制（ACC）车辆和协同自适应巡航控制（CACC）车辆的行车行为特征分析的基础上，运用跟驰模型和换道模型分别构建人工驾驶车辆、ACC车辆及CACC车辆在下匝道分流区混合交通流仿真环境，解析CACC车辆占比对混合交通流安全性的影响。选取全速度差模型、ACC跟驰模型、CACC跟驰模型分别作为人工驾驶车辆、ACC车辆、CACC车辆的纵向跟驰模型，利用随意换道模型、强制换道模型分别构建下匝道分流主线段、远近端区的横向换道模型。基于碰撞时间（TTC）、暴露碰撞时间（TET）、整合碰撞时间（TIT）等参数构建交通流安全性评价指标。利用MATLAB进行数值模拟，仿真分析不同CACC车辆占比下的混合交通流安全性。结果表明：CACC车辆占比为40%~50%时，混合交通流安全性恶化最严重，TET和TIT分别增加约68%和89%，车辆速度离散系数为0.9以上；通过在下匝道分流区设置远端强制换道区（设置长度≤ 1 000 m），可有效降低混合交通流的追尾碰撞风险。Abstract: Based on the analyses of driving behavior of manually operated, adaptive cruise control(ACC), and cooperative adaptive cruise control(CACC)vehicles, this paper investigates the impact of proportion of CACC vehicles on the safety of mixed-traffic-flow at off-ramp diverging areas in a simulated environment, which is established based on a car-following model and a lane-changing model. Specifically, a full velocity difference model, an ACC car-following model and a CACC car-following model are used as the longitudinal car-following models for manually operated, ACC, and CACC vehicles, respectively. A discretionary lane-changing model and a mandatory lane-changing model are customized to develop the lateral lane-changing model for all types of vehicles at the main and end sections of off-ramp diverging areas, respectively. Next, a set of evaluation indices for traffic safety are proposed based on the following parameters such as time-to-collision(TTC), time exposed time-to-collision(TET) and time integrated time-to-collision(TIT). The MATLAB software is used to analyze the safety for mixed traffic flows under the scenarios with different proportions of CACC vehicles. The results show that: when the proportion of CACC vehicles ranges between 40% and 50%, the safety of mixed traffic flow deteriorates most, TET and TIT increase by about 68% and 89%, respectively, and the speed dispersion coefficient is as large as more than 0.9. Study results also indicate that the risk of rear-end collision for mixed traffic flow can be effectively reduced by adding the mandatory lane-changing area(≤ 1 000 m)at the far-end of off-ramp diverging area.
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图 1 高速公路下匝道分流区仿真场景
Figure 1. Simulation scene of off-ramp diverging area of expressway
图 2 下匝道分流区混合交通流换道流程图
Figure 2. Flow chartofmixed traffic flow lane-changing in off-ramp diverging area
图 3 车辆协助换道过程示意图
Figure 3. Schematic diagram of the vehicle assisted lane-changing process
图 4 不同PCACC和TTC时车辆数统计分布图
Figure 4. Statistical distribution ofthe number of vehicles under different PCACC and TTC
图 6 不同PCACC下速度标准差变化曲线
Figure 6. The curve of speed standard deviation under different PCACC
图 7 不同长度远端强制换道区下的TET值变化曲线
Figure 7. Thecurve of TET value under different lengths of the far-end mandatory lane-changing area
图 8 不同PCACC和Lfar对应的TTC时空热力图
Figure 8. Temporal and spatial heat map of TTC under different Pcacc and Lfar
图 9 不同PCACC和Lfar下的2个区域换道次数占比变化曲线
Figure 9. The curve of the ratio of lane changes in the two areas under different Pcacc and Lfar
表 1 全速度差模型参数取值
Table 1. The parameters value of FVD
参数 取值 κ/s-1 0.629 λ/s-1 4.10 α/s-1 1.26 vf/(m/s) 33.0 s0/m 2.46 l/m 5.00 
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表 2 不同PCACC时0≤TTC ≤20对应的车辆数及其相对比例
Table 2. The number of vehicles corresponding to 0≤ TTC ≤20 under different PCACC and their relative proportions
PCACC 车辆数/辆（0 ≤ TTC ≤ 20） 较于PCACC = 0变化比例/% 0 1 137 0 0.2 2 127 87.0 0.4 2 541 123.4 0.6 2 054 80.6 0.8 1 716 50.9 1.0 922 -18.9
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[1] XIAO L, GAO F. A comprehensive review of the development of adaptive cruise control systems[J]. Vehicle System Dynamics, 2010, 48(10): 1167-1192. doi: 10.1080/00423110903365910 [2] SHAOUT A, TARRAH M A. Cruise control technology review[J]. Computers and Electrical Engineering, 1997, 23(4): 259-271. doi: 10.1016/S0045-7906(97)00013-X [3] 王殿海, 金盛. 车辆跟驰行为建模的回顾与展望[J]. 中国公路学报, 2012, 25(1): 115-127. doi: 10.3969/j.issn.1001-7372.2012.01.018WANG D H, JIN S. Review and prospect of modeling of car-following behavior of vehicles[J]. China Journal of Highway and Transport, 2012, 25(1): 115-127. (in Chinese) doi: 10.3969/j.issn.1001-7372.2012.01.018 [4] 陆建, 李英帅. 车辆换道行为建模的回顾与展望[J]. 交通运输系统工程与信息, 2017, 17(4): 48-55. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201704008.htmLU J, LI Y S. Retrospect and prospect of vehicle lane changing behavior modeling[J]. Transportation System Engineering and Information, 2017, 17(4): 48-55. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201704008.htm [5] JIANG R, WU Q, ZHU Z. Full velocity difference model for a car-following theory[J]. Physical Review E, 2001, 64(1): 017101. doi: 10.1103/PhysRevE.64.017101 [6] TREIBER M, HENNECKE A, HELBING D. Congested traffic states in empirical observations and microscopic simulations[J]. Physical Review E, 2000, 62(2): 1805. doi: 10.1103/PhysRevE.62.1805 [7] SHLADOVER S E, SU D, LU X Y. Impacts of cooperative adaptive cruise control on freeway traffic flow[J]. Transportation Research Record, 2012, 2324(1): 63-70. doi: 10.3141/2324-08 [8] 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. http://hal.archives-ouvertes.fr/hal-01091160/file/CACCmodel_TRC_Final.pdf [9] AHMED K, BEN-AKIVA M, KOUTSOPOULOS H, et al. Models of freeway lane changing and gap acceptance behavior[J]. Transportation and Traffic Theory, 1996(13): 501-515. [10] RICKERT M, NAGEL K, SCHRECKENBERG M, et al. Two lane traffic simulations using cellular automata[J]. Physica A: Statistical Mechanics and its Applications, 1996, 231(4): 534-550. doi: 10.1016/0378-4371(95)00442-4 [11] CHOWDHURY D, WOLF D E, SCHRECKENBERG M. Particle hopping models for two-lane traffic with two kinds of vehicles: Effects of lane-changing rules[J]. Physica A: Statistical Mechanics and its Applications, 1997, 235(3/4): 417-439. http://www.onacademic.com/detail/journal_1000034194207010_2298.html [12] 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. doi: 10.11860/j.issn.1673-0291.20190045JIANG Y S, HU R, YAO Z H, et al. Analysis of the stability and safety of heterogeneous traffic flow in the intelligent networked vehicle environment[J]. Journal of Beijing Jiaotong University, 2020, 44(1): 27-33. (in Chinese) doi: 10.11860/j.issn.1673-0291.20190045 [13] 秦严严, 王昊, 王炜. 网联辅助驾驶混合交通流稳定性及安全性分析[J]. 东南大学学报(自然科学版), 2018, 48(1): 188-194. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201801029.htmQIN Y Y, WANG H, WANG W. Stability and safety analysis of mixed traffic flow for network-assisted driving[J]. Journal of Southeast University (Natural Science Edition), 2018, 48(1): 188-194. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201801029.htm [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. http://www.sciencedirect.com/science?_ob=ShoppingCartURL&_method=add&_eid=1-s2.0-S0378437115000837&originContentFamily=serial&_origin=article&_ts=1488882894&md5=c82e461cf3822d09f265ffb3c09f06ae [15] NGODUY D. Analytical studies on the instabilities of heterogeneous intelligent traffic flow[J]. Communications in Nonlinear Science and Numerical Simulation, 2013, 18(10): 2699-2706. doi: 10.1016/j.cnsns.2013.02.018 [16] HORIGUCHI R, OGUCHI T. A Study on car following models simulating various adaptive cruise control behaviors[J]. International Journal of Intelligent Transportation Systems Research, 2014, 12(3): 127-134. doi: 10.1007/s13177-013-0077-5 [17] 王祺, 谢娜, 侯德藻, 等. 自适应巡航及协同式巡航对交通流的影响分析[J]. 中国公路学报, 2019, 32(6): 188-197, 205. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906020.htmWANG Q, XIE N, HOU D Z, et al. Analysis of the influence of adaptive cruise and cooperative cruise on traffic flow[J]. China Journal of Highway and Transport, 2019, 32(6): 188-197+205. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906020.htm [18] 徐桃让, 姚志洪, 蒋阳升, 等. 智能网联车环境下考虑反应时间影响的基本图模型[J]. 公路交通科技, 2020, 37(8): 108-117. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK202008014.htmXU T R, YAO Z H, JIANG Y S, et al. The basic graph model considering the impact of reaction time in the intelligent networked vehicle environment[J]. Highway and Transportation Science and Technology, 2020, 37(8): 108-117. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK202008014.htm [19] MAHDINIA I, ARVIN R, KHATTAK A J, et al. Safety, energy, and emissions impacts of adaptive cruise control and cooperative adaptive cruise control[J]. Transportation Research Record, 2020, 2674(6): 253-267. doi: 10.1177/0361198120918572 [20] 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. doi: 10.3963/j.jssn.1674-4861.2021.05.010MA Q L, FU B Y, ZENG H W. Basic diagram and stability analysis of heterogeneous traffic flow in the environment of intelligent network connection[J]. Journal of Transport Information and Safety, 2021, 39(5): 76-84.(in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.05.010 [21] 秦严严, 王昊, 王炜, 等. 混有CACC车辆和ACC车辆的异质交通流基本图模型[J]. 中国公路学报, 2017, 30(10): 127-136. doi: 10.3969/j.issn.1001-7372.2017.10.016QIN Y Y, WANG H, WANG W, et al. Basic graph model of heterogeneous traffic flow mixed with CACC vehicles and ACC vehicles[J]. Journal of Highway and Transport, 2017, 30(10): 127-136. (in Chinese) doi: 10.3969/j.issn.1001-7372.2017.10.016 [22] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201802010.htmQIN Y Y, WANG H, RAN B. CACC car-following modeling and mixed traffic flow analysis[J]. Transportation System Engineering and Information, 2018, 18(2): 60-65. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201802010.htm [23] YE L, YAMAMOTO T. Evaluating the impact of connected and autonomous vehicles on traffic safety[J]. Physica A: Statistical Mechanics and its Applications, 2019(526): 121009. http://www.onacademic.com/detail/journal_1000042280479999_b1cd.html [24] 吴德华, 陈家伟, 彭锐, 等. 快速路上匝道瓶颈路段异质交通流演变规律[J]. 贵州大学学报(自然科学版), 2020, 37(4): 99-104. https://www.cnki.com.cn/Article/CJFDTOTAL-GZDI202004018.htmWU D H, CHEN W, PENG R, et al. Evolution of heterogeneous traffic flow in the bottleneck section of ramp on expressway[J]. Journal of Guizhou University (Natural Science Edition), 2020, 37(4): 99-104. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GZDI202004018.htm [25] 康瑞, 杨凯. 敏感换道对下匝道系统交通流的影响[J]. 物理学报, 2013, 62(23): 455-462. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201323064.htmKANG R, YANG K. The impact of sensitive lane changing on off-ramp system traffic flow[J]. Acta Physica Sinica, 2013, 62(23): 455-462. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201323064.htm [26] LI Y, WANG H, WANG W, et al. Evaluation of the impacts of cooperative adaptive cruise control on reducing rear-end collision risks on freeways[J]. Accident Analysis & Prevention, 2017(98): 87-95. http://www.researchgate.net/profile/Ye_Li21/publication/308876206_Evaluation_of_the_impacts_of_cooperative_adaptive_cruise_control_on_reducing_rear-end_collision_risks_on_freeways/links/58cd35b34585157b6dae3eba/Evaluation-of-the-impacts-of-cooperative-adaptive-cruise-control-on-reducing-rear-end-collision-risks-on-freeways.pdf -
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