An Iterative Learning Control Strategy for Traffic Signals Considering NonlinearCharacteristics of Traffic Flow
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摘要: 现实中城市交通流的运行具有很强的非线性特性, 采用简单的线性模型难以全面描述交通流的实际运行过程. 本文在考虑城市交通流非线性动态特性的基础上, 提出了一种非线性交通流排队模型, 并基于宏观交通流固有的周期性特征, 设计了交叉口信号的迭代学习控制策略. 通过对交叉口信号的迭代学习控制, 使交叉口各进口道的车辆排队长度尽可能趋于均衡, 提高交叉口信号有效绿灯时间的利用率, 从而提高路网的通行效率. 最后通过严格的数学推导证明了该方法的收敛性, 仿真研究及实验结果验证了所提方法的有效性.Abstract: The operation of urban traffic flow has strong non-linear characteristics in real traffic situation, so it is difficult to describe the actual operation process of traffic flow comprehensively by using simple linear model. Considering the nonlinear dynamic characteristics of urban traffic flow, a nonlinear traffic flow queuing model is proposed, and an iterative learning control strategy for intersection signals is designed based on the inherent periodic characteristics of macroscopic traffic flow. By the iterative learning control of intersection signals, the vehicle queue lengths in the import way are balanced as far as possible, thus the utilization rate of effective green light time at intersections and the efficiency of road network are effectively improved. Finally, the convergence of the method is proved by strict mathematical deduction, and the effectiveness of the proposed method is verified by the simulation research and experimental results.
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图 3 三种控制方案下各交叉口相位车辆排队长度差值
Fig. 3 The differences of queue lengths at different phases of each intersection for the three control schemes
图 4 三种控制方案下交叉口4各相位的车辆排队情况
Fig. 4 The queue lengths at Intersection 4 for the three control schemes
图 5 三种控制方案下交叉口9各相位的车辆排队情况
Fig. 5 The queue lengths at Intersection 9 for the three control schemes
图 6 平均车辆排队长度差值随迭代次数的变化情况
Fig. 6 The average difference of queue lengths of allintersections after each iteration
图 7 三种控制方案下路网的平均延迟时间
Fig. 7 The average delay time of road network for thethree control schemes
图 9 三种控制方案下路网内车辆的平均速度
Fig. 9 The average speed of vehicles for the threecontrol schemes
表 1 各路段车道数
Table 1 The number of lanes in each link
道路情况 道路编号 双向 3 车道道路 $x_{1},x_{2},x_{3},x_{4},x_{9},x_{10},x_{11},x_{14},x_{17},x_{20},x_{23}$ 双向 4 车道道路 $x_{12},x_{13},x_{15},x_{16},x_{18},x_{19},x_{21},x_{22}$ 双向 7 车道道路 $x_{5},x_{6},x_{7},x_{8}$ 
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表 2 路网的输入流量(veh/h)
Table 2 The inflows of the road network (veh/h)
时段/min $x_{1},x_{4},x_{9},x_{12},x_{14},x_{21},x_{23}$ $x_{13},x_{22}$ $x_{5},x_{8}$ $0\sim20$ 1 600 3 000 2 000 $20\sim40$ 2 000 3 400 2 400 $40\sim60$ 1 800 3 200 2 200
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表 3 交叉口的迭代学习增益
Table 3 The iterative learning gains at different intersections
交叉口编号 学习增益值 1, 2, 3, 4, 5, 6, 7, 8, 9 0.1
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