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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