Self-organizing method for traffic coupling between adjacent ramps in intelligent and connected environments
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摘要: 为改善智能网联环境下匝道合流区通行安全与效率问题，提出了一种近邻匝道交通耦合自组织方法，通过建立主线车流间隙与匝道车辆速度优化匹配模型，以实现对主线最外侧车道总体车头间距的优化调整，在保证汇入安全的前提下提升了近邻匝道内的车辆通行效率；选取重庆市内环快速路上东环立交附近2个近邻匝道为研究原型，利用在线地图结合无人机航拍以及定点摄像等数据采集方式对试验路段进行实地调查；在智能网联环境下，分别运用协同自适应巡航控制(CACC)和交通耦合自组织方法(TCS)，采用Python、SUMO和TraCI对试验路段车辆运行情况进行联合仿真。研究结果表明：相较于CACC，TCS的换道次数从65.52次降至52.64次，下降了19.87%，有效缓解了近邻匝道内的交通冲突；平均延误从24.53 s降至14.39 s，下降了70.38%，其中平峰期降低了77.71%，高峰期只降低了34.50%，相较于高峰期，在平峰期的运行效率提升较大；时间占有率从18.70%降至8.63%，下降了53.86%，不同车道间的时间占有率之差降低至6.00%，即车辆在不同车道上的分布更平均；平均速度从78.31 km·h-1上升至80.78 km·h-1，提升了3.06%，有效缓解了合流区和分流区附近的减速情况。Abstract: A self-organizing method for traffic coupling between adjacent ramps was proposed to improve traffic safety and efficiency in on-ramp merging areas in intelligent and connected environments. By establishing an optimal matching model between the mainline traffic flow gap and on-ramp vehicle speed, the overall headway of the outermost lane of the mainline was optimized and adjusted. On the premise of ensuring the safe merging of on-ramp vehicles, the traffic efficiency of vehicles between adjacent ramps was improved. Two adjacent ramps near the Donghuan overpass on the inner ring expressway in Chongqing were selected as the research prototype. Online map combined with drone aerial photography, fixed-point photography, and other data acquisition methods were used to conduct field investigations on the testing sections. In intelligent and connected environments, cooperative adaptive cruise control (CACC) and traffic coupling self-organizing (TCS) method were applied respectively, and Python, SUMO, and TraCI were used for co-simulation of vehicle operation on the test road. Research results show that compared with CACC, the lane changing number in TCS decreases by 19.87% from 65.52 to 52.64, which effectively alleviates the traffic conflict between adjacent ramps. The average delay decreases by 70.38% from 24.53 s to 14.39 s. To be specific, the average delay decreases by 77.71% in the off-peak period and 34.50% in the peak period, respectively. Compared with the peak period, the efficiency in the off-peak period is greatly improved. The time occupancy decreases by 53.86% from 18.70% to 8.63%. The time occupancy difference between different lanes decreases to 6.00%, that is, vehicles are more evenly distributed across different lanes. The average speed increases by 3.06% from 78.31 km·h-1 to 80.78 km·h-1, which effectively alleviates the deceleration near the on-ramp merging and off-ramp diverging areas.
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图 3 东环立交近邻匝道交织区道路结构
Figure 3. Road structure of adjacent ramps in East Ring Interchange
图 4 东环立交近邻匝道交织区交通调查数据
Figure 4. Adjacent ramps traffic survey data for East Ring Interchange
表 1 CACC和TCS的换道次数统计
Table 1. Lane changing times statistics under CACC and TCS
时间 K1 K′1 K2 K′2 K3 K′3 ρ1/% ρ2/% ρ3/% 7:00 135 103 81 68 130 101 23.70 16.05 22.31 8:00 161 111 99 77 134 109 31.06 22.22 18.66 9:00 155 124 78 60 122 93 20.00 23.08 23.77 10:00 138 112 76 70 102 86 18.84 7.89 15.69 11:00 65 54 33 30 52 43 16.92 9.09 17.31 12:00 26 21 20 17 15 13 19.23 15.00 13.33 13:00 28 23 23 20 14 12 17.86 13.04 14.29 14:00 29 25 24 21 19 16 13.79 12.50 15.79 15:00 44 39 20 18 29 24 11.36 10.00 17.24 16:00 57 46 28 25 20 17 19.30 10.71 15.00 17:00 75 60 75 61 32 24 20.00 18.67 25.00 18:00 129 91 88 72 58 45 20.93 18.18 22.41 19:00 114 84 79 67 69 55 29.46 15.19 20.29 20:00 65 51 67 60 35 29 26.32 10.45 17.14 21:00 44 35 33 29 33 28 20.45 12.12 15.15 
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表 2 CACC和TCS交通参数统计
Table 2. Traffic parameter statistics under CACC and TCS
参数 Kε/次 TWε, j/s RAi, j/% VAi, j/(km·h-1) CACC 65.52 24.53 18.70 78.31 TCS 52.64 14.39 8.63 80.78
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