Cooperative merging control of connected and automated vehicles in merging area for one-way three-lane freeway
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摘要: 为提高多车道高速公路入口匝道智能网联车辆合流安全性与通行效率，提出了一种基于规则的换道策略和一个时间离散的车辆轨迹优化模型，以进行协同汇入控制；以普通单向三车道高速公路匝道合流区为研究对象，将合流区及其上下游路段划分为4个区域，并对关键的提前换道区和协同合流区分别进行交通控制；在上游提前换道区，基于最小安全跟车间距和速度效益的换道规则，将部分主线外侧车道、中间车道的车辆在合流区上游提前换道至内侧相邻车道，以此减轻合流区外侧车道的交通压力，提高合流效率；在下游协同合流区，选取合适的周期时长，以周期内合流车辆行驶速度最大为目标，不固定合流点，规划合流车辆的纵向行车轨迹，引导匝道车辆在周期结束后汇入主线，实现协同合流；利用SUMO和Python仿真验证提出的协同汇入控制方法，并进行临界跟驰车头间距的敏感性分析。仿真结果表明：与无控制自然合流相比，提出的协同汇入控制方法在不同的交通需求水平下能使车辆平均速度提高4.9%~21.1%，平均延误降低29.9%~56.5%，且不会出现停车现象；与先进先出合流控制相比，在高匝道交通需求水平下能使车辆平均速度提高3.4%~9.6%，平均延误降低22.9%~39.4%；较低的临界跟驰车头间距可以更好地提高合流区通行效率，且在主线交通需求水平较高时更明显。Abstract: In order to improve the merging safety and traffic efficiency of connected and automated vehicles (CAVs) at multilane freeway on-ramps, a rule-based lane-changing strategy and a discrete-time vehicle trajectory optimization model were proposed to implement cooperative merging control. With the merging area for common one-way three-lane freeway ramps as the research object, the merging area and its upstream and downstream sections were divided into four areas, and traffic control was carried out in the key lane-changing area and cooperative merging area. In the upstream lane-changing area, based on the lane change rules of minimum safe following headway and speed benefit, the vehicles in the outer lane and the middle lane of part of the mainline could change to the adjacent inner lanes in the upstream of the merging area in advance, so as to reduce the traffic pressure on the outer lane of the merging area and improve the merging efficiency. In the downstream cooperative merging area, the appropriate cycle time was chosen. With the maximum speed of merging vehicles during the cycle time as the goal, the longitudinal trajectory of the merging vehicle was planned without a fixed merging point, and the ramp vehicles were guided to merge into the mainline at the end of the cycle, so as to realize cooperative merging. The cooperative merging control method was verified by SUMO and Python simulation, and the sensitivity analysis of critical car-following headway was conducted. Simulation results show that compared with uncontrolled natural merging, the proposed cooperative merging control method can increase the average speed of vehicles by 4.9%-21.1% and reduce the average delay of vehicles by 29.9%-56.5% under different traffic demand levels, and it ensures that no vehicle stops. Compared with first-in first-out merging control, the proposed cooperative merging control method can increase the average speed of vehicles by 3.4%-9.6% and reduce the average delay of vehicles by 22.9%-39.4% under high ramp traffic demand levels. Furthermore, the lower critical car-following headway can improve the traffic efficiency in the merging area, and it is more obvious under the high demand level of mainline traffic.
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图 1 单向三车道高速公路协同合流场景
Figure 1. Cooperative merging Scenarios of one-way three-lane freeway
图 8 不同主线交通需求水平下车辆平均速度对比
Figure 8. Comparison of average speeds of vehicles under different mainline traffic demand levels
图 9 不同主线交通需求水平下车辆平均延误对比
Figure 9. Comparison of average delays of vehicles under different mainline traffic demand levels
图 10 不同的临界跟驰车头间距下车辆平均速度对比
Figure 10. Comparison of average speeds of vehicles under different critical car-following headways
图 11 不同的临界跟驰车头间距下车辆平均延误对比
Figure 11. Comparison of average delays of vehicles under different critical car-following headways
表 1 仿真试验参数
Table 1. Simulated experimental parameters
参数 数值 参数 数值 t0/s 1.2 vr_max/(m·s-1) 16.7 s0/m 2 amax/(m·s-2) 5 L/m 5 amin/(m·s-2) -5 H1/m 200 Amax/(m·s-2) 3 H2/m 300 Dmin/m 8 H3/m 300 Δk/s 1 H4/m 200 T/s 6 vm_max/(m·s-1) 25 
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表 2 不同交通需求水平下车辆平均速度对比
Table 2. Comparison of average speeds of vehicles under different traffic demand levels
km·h-1 匝道交通量/(veh·h-1) 控制方式 主线交通量/(veh·h-1) 800 1 200 1 600 2 000 600 无控制 77.40 74.85 70.99 69.37 仅轨迹优化控制 78.88(+1.9%) 76.43(+2.1%) 75.13(+5.8%) 74.48(+7.4%) 协同汇入控制 80.10(+3.5%) 77.72(+3.8%) 76.24(+7.4%) 75.76(+9.2%) 800 无控制 75.56 72.07 68.65 65.12 仅轨迹优化控制 77.47(+2.5%) 76.01(+5.5%) 75.02(+9.3%) 74.45(+14.3%) 协同汇入控制 79.07(+4.6%) 77.68(+7.8%) 76.18(+11.0%) 75.59(+16.1%) 1 000 无控制 73.44 71.24 67.00 64.30 仅轨迹优化控制 77.25(+5.2%) 75.28(+5.7%) 74.27(+10.8%) 73.84(+14.8%) 协同汇入控制 78.90(+7.4%) 77.60(+8.9%) 75.94(+13.3%) 75.43(+17.3%) 1 200 无控制 71.50 69.84 64.73 55.01 仅轨迹优化控制 76.86(+7.5%) 75.05(+7.5%) 73.26(+13.2%) 71.49(+30.0%) 协同汇入控制 78.26(+9.5%) 77.30(+10.7%) 75.68(+16.9%) 75.19(+36.7%)
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表 3 不同交通需求水平下车辆平均延误对比
Table 3. Comparison of average delays of vehicles under different traffic demand levels s·veh-1
匝道交通量/(veh·h-1) 控制方式 主线交通量/(veh·h-1) 800 1 200 1 600 2 000 600 无控制 1.44 2.02 2.70 3.05 仅轨迹优化控制 1.27(-11.8%) 1.68(-16.8%) 1.98(-26.7%) 2.08(-31.8%) 协同汇入控制 1.10(-23.6%) 1.44(-28.7%) 1.76(-34.8%) 1.87(-38.7%) 800 无控制 1.77 2.43 3.22 4.10 仅轨迹优化控制 1.54(-13.0%) 1.80(-25.9%) 1.99(-38.2%) 2.11(-48.5%) 协同汇入控制 1.27(-28.2%) 1.48(-39.1%) 1.77(-45.0%) 1.91(-53.4%) 1 000 无控制 2.20 2.63 3.61 4.30 仅轨迹优化控制 1.56(-29.1%) 1.93(-26.6%) 2.13(-41.0%) 2.21(-48.6%) 协同汇入控制 1.31(-40.4%) 1.52(-42.2%) 1.84(-49.0%) 1.93(-55.1%) 1 200 无控制 2.60 2.93 4.17 5.89 仅轨迹优化控制 1.65(-36.5%) 1.94(-33.8%) 2.30(-44.8%) 2.58(-56.2%) 协同汇入控制 1.45(-44.2%) 1.59(-45.7%) 1.88(-54.9%) 1.95(-66.9%)
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表 4 不同交通需求水平下车辆总停车次数对比
Table 4. Comparison of total stops of vehicles under different traffic demand levels
匝道交通量/(veh·h-1) 控制方式 主线交通量/(veh·h-1) 800 1 200 1 600 2 000 600 无控制 0 0 0 0 仅轨迹优化 0 0 0 0 协同汇入控制 0 0 0 0 800 无控制 0 0 2 9 仅轨迹优化 0 0 0 0 协同汇入控制 0 0 0 0 1 000 无控制 0 0 5 24 仅轨迹优化 0 0 0 0 协同汇入控制 0 0 0 0 1 200 无控制 0 4 10 68 仅轨迹优化 0 0 0 3 协同汇入控制 0 0 0 0
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