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摘要: 为缓解交通堵塞, 基于人工智能的强化学习理论, 提出了不完全信息下的强化学习单点入口匝道控制方法(RLRM)。基于6个仿真实例, 分别计算了平均速度、平均密度、流出交通量与旅行时间, 比较了无控制、定时控制与RLRM控制的控制效果。仿真结果表明: 在交通量较小的实例1中, 以旅行时间为评价指标, 定时控制与RLRM控制的交通阻塞缓解率分别为-6.25%、-9.38%, 几乎没有控制效果; 在交通量变大的实例3中, 以旅行时间为评价指标, 定时控制与RLRM控制的交通阻塞缓解率分别为-8.19%、3.51%, 匝道控制有一定效果, RLRM控制略优于定时控制; 在交通量最大的实例6中, 以平均速度、平均密度、流出交通量与旅行时间为评价指标, 定时控制的交通阻塞缓解率分别为8.20%、0.39%、18.97%与23.99%, RLRM控制的交通阻塞缓解率分别为18.18%、3.42%、30.65%与44.41%, RLRM控制明显优于定时控制。可见, 交通量越大, RLRM控制效果越明显。Abstract: In order to relieve freeway traffic congestion, reinforcement learning ramp metering(RLRM) control method for single entrance ramp of highway under the incomplete information was proposed based on the artificial intelligence theories of reinforcement learning.Average speeds, average densities, traffic outflows and travel times of numerical cases 1-6 were calculated, and the control effect of RLRM was compared with no control and fixed-time control.Simulation result shows that in case 1 with the lowest traffic inflow, the congestion relief rates of fixed-time control and RLRM control depending on travel time are-6.25% and-9.38% respectively, which indicates that the control effect is not significant.When the traffic inflow increases in case 3, the congestion relief rates of fixed-time control and RLRM control depending on travel time are-8.19% and 3.51% respectively, which indicates that the control has some effect, and RLRM control performs better than fixed-time control.In case 6 with the highest traffic inflow, the congestion relief rates of fixed-time control are 8.20%, 0.39%, 18.97% and 23.99% respectively, and those of RLRM control are 18.18%, 3.42%, 30.65% and 44.41% taking average speed, average density, traffic outflow and travel time as evaluating indexes respectively, which shows that RLRM control effect is more significant than fixed-time control.So the greater the traffic inflow is, the better the control effect of RLRM is.
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Key words:
- traffic control /
- ramp /
- traffic flow simulation /
- artificial intelligence /
- reinforcement learning /
- RLRM control
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图 4 实例1中平均密度与交通阻塞缓解率
Figure 4. Average densities and congestion relief rates in case 1
表 1 控制参数
Table 1. Control parameters
Tmax/min R/(pcu·h-1) rmax/(pcu·h-1) rn/(pcu·h-1) Nr 5 200 1 100 100 11 
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表 2 交通流量参数
Table 2. Traffic flow parameters pcu·h-1
实例 1 2 3 4 5 6 主线流入交通量 1 200 1 500 1 800 1 800 2 500 2 500 匝道流入交通量 300 300 600 900 600 900 交通总量 1 500 1 800 2 400 2 700 3 100 3 400
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表 3 实例2控制评价指标
Table 3. Control evaluation indexes in case 2
控制类型 主线平均速度/(km·h-1) 主线平均密度/(pcu·km-1) 流出交通量/(pcu·h-1) 旅行时间/s 无控制 108.85 28.32 2 056 95 定时控制 101.61 33.97 1 763 119 RLRM控制 101.79 34.57 2 068 100
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表 4 实例4控制评价指标
Table 4. Control evaluation indexes in case 4
控制类型 主线平均速度/(km·h-1) 主线平均密度/(pcu·km-1) 流出交通量/(pcu·h-1) 旅行时间/s 无控制 63.57 77.78 2 445 503 定时控制 63.73 94.38 2 639 466 RLRM控制 72.18 71.41 2 701 311
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表 5 实例5控制评价指标
Table 5. Control evaluation indexes in case 5
控制类型 主线平均速度/(km·h-1) 主线平均密度/(pcu·km-1) 流出交通量/(pcu·h-1) 旅行时间/s 无控制 53.42 109.33 2 054 558 定时控制 54.86 101.02 2 311 407 RLRM控制 64.37 98.79 3 176 333
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