Regional Traffic Signal Control Considering the Dynamic Characteristics of Traffic Flow
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摘要: 针对区域交通信号，考虑智能交通系统中交通流的动态特性，提出了区域交通系统改进的存储-转发模型.考虑大型区域的复杂性和协调性，将区域交通划分成N个子区域，分别建立了对应的子区域模型.针对子区域模型，提出了基于分层模型预测控制的过饱和区域交通信号控制优化目标.通过引入拉格朗日对偶原理解决约束条件问题的方法，对子区域的车辆排队数量进行了预测，并对有效绿灯时间进行优化控制.为了验证所提区域交通控制算法的有效性，给出了本文改进的模型与存储-转发模型的对比仿真.实验结果表明，在达到相同的控制效果时，本文改进模型的控制算法所需的计算时间较短，计算成本较低.Abstract: In this paper, regional traffic signal control is studied. Considering the dynamic characteristics of traffic flow, an improved store-forward model is proposed. Due to the complexity and coordination of regional traffic, a large-scale regional traffic is divided into several sub-regions using a region decomposition method firstly. Secondly, the optimization problem of the large-scale regional traffic is presented based on Hico-MPC. Lagrange dual theory multipliers are introduced to deal with interconnecting constraints among sub-regions. Then we forecast the vehicle queue of sub-regions and optimize the green light time. Finally, the simulation is presented to illustrate the effectiveness of the improved model by comparing with the store-forward model. The results show that the improved model requires less computation time and lower computational cost when the same control effect is achieved.
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Key words:
- Regional traffic /
- the dynamic characteristics /
- improved model /
- hierarchical MPC
1) 本文责任编委 赵勇 -
图 1 交通流$r$的交通动态图
((a)交通流无外界输入; (b)交通流有外界输入(虚线))
Fig. 1 Traffic dynamics
((a) Traffic without input; (b) Traffic with inputdotted lines)
图 2 区域分解的例子
((a)实验区域; (b)实验区域的分解情况)
Fig. 2 Example of network decomposition
((a) A test network; (b) The decomposition of (a))
图 11 本文改进模型控制的交通流$x_{3}$的变化
Fig. 11 The changing of traffic $x_{3}$ under proposed model
图 12 存储转发模型控制的交通流$x_{3}$的变化
Fig. 12 The changing of traffic $x_{3}$ under store and forward model
图 13 本文改进模型控制的交通流$x_{7}$的变化
Fig. 13 The changing of traffic $x_{7}$ under proposed model
图 14 存储转发模型控制的交通流$x_{7}$的变化
Fig. 14 The changing of traffic $x_{7}$ under store and forward model
表 1 基本参数的定义
Table 1 Definitions of basic parameters
参数 变量 仿真值 周期时长 $C$ 120 s 损失时间 $L$ 20 s 控制间隔 $T$ 120 s 车辆平均长度 $l$ 5 m 
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2(a) 测试网中的转弯率
2(a) Turning rates of the test network
${\tau _{w, r}}$ $x_1$ $x_2$ $x_3$ $x_4$ $x_5$ $x_6$ $x_1$ 0 0 0 0.2 0 0.5 $x_2$ 0 0 0 0.15 0 0.35 $x_3$ 0 0 0 0.5 0 0.15 $x_4$ 0 0 0 0 0 0 $x_5$ 0 0 0 0 0 0 $x_6$ 0 0 0 0 0.3 0 $x_7$ 0 0 0 0 0.6 0 $x_8$ 0 0 0 0 0 0 $x_9$ 0 0 0 0 0 0 $x_{10}$ 0 0 0 0 0 0 $x_{11}$ 0 0 0 0 0 0
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2(b) 测试网中的转弯率
2(b) Turning rates of the test network
${\tau _{w, r}}$ $x_7$ $x_8$ $x_9$ $x_{10}$ $x_{11}$ $x_{12}$ $x_{13}$ $x_1$ 0 0 0 0 0.15 0 0.5 $x_2$ 0 0 0 0 0.35 0 0.15 $x_3$ 0 0 0 0 0.2 0 0.15 $x_4$ 0 0 0 0 0 0 0 $x_5$ 0 0 0 0 0 0 0 $x_6$ 0 0 0 0 0 0 0 $x_7$ 0 0 0 0 0 0 0 $x_8$ 0.4 0 0 0.6 0 0 0 $x_9$ 0.6 0 0 0.4 0 0 0 $x_{10}$ 0 0 0 0 0 0.7 0 $x_{11}$ 0 0 0 0 0 0.3 0
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表 3 初始状态和扰动
Table 3 initial states and disturbances
$\text{定义}$ $\text{描述}$ $\text{范围}$ $I_{L}$ $\text{低初态}$ $x_{i}^{6, 11}\in[25, 50), x_{i}^{4, 13}\in[20, 40), x_{i}^{\rm other}\in[10, 20)$ $I_{M}$ $\text{中初态}$ $x_{i}^{6, 11}\in[45, 80), x_{i}^{4, 13}\in[35, 60), x_{i}^{\rm other}\in[15, 30)$ $I_{H}$ $\text{高初态}$ $x_{i}^{6, 11}\in[60, 100), x_{i}^{4, 13}\in[50, 80), x_{i}^{\rm other}\in[20, 40)$ $e_{L}$ $\text{低扰动}$ $e_{i}^{6, 11}\in[5, 10), e_{i}^{4, 13}\in[4, 8), e_{i}^{\rm other}\in[2, 4)$ $e_{H}$ $\text{高扰动}$ $e_{i}^{6, 11}\in[8, 15), e_{i}^{4, 13}\in[6, 10), e_{i}^{\rm other}\in[4, 6)$ 注: $x_i^{6, 11}$表示交通流$\{{x_6}, {x_{11}}\} $现有的排队长度, $x_i^{4, 13}$表示交通流$\{{x_4}, {x_{13}}\} $现有的排队长度, ${x^{\rm other}}$表示其他交通流现有的排队长度.同理$x_i^{6, 11}$, $x_i^{4, 13}$, ${x^{\rm other}}$表示相应交通流的扰动.
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