城市区域交通信号迭代学习控制策略

城市交通流具有复杂的非线性动态特性, 在交通控制中难以对其进行精确的数学建模; 同时, 以天为周期, 宏观交通流又呈现出明显的周期性特征. 鉴于此, 提出一种基于迭代学习的城市区域交通信号控制策略, 通过对交通信号的迭代控制, 使路段的平均占有率收敛于期望占有率, 从而使绿灯时间得到充分利用并防止交通拥堵的发生, 保证了交通流在路网中的高效平稳运行. 严格的理论推导证明了该方法的收敛性, 仿真结果验证了该方法的有效性.

Iterative learning control strategy for traffic signal of urban area

The urban traffic flow has complex nonlinear dynamic behavior. It is very difficult to precisely model it in urban traffic control. Meanwhile, the macroscopic traffic flow appears apparent cyclical characteristics in one day cycle. Therefore, an iterative learning control strategy for signal timing of urban regional traffic is proposed. Through iterative control of the traffic signals, the average road occupancy rates in the regional traffic network achieve the desired ones. Thus, the green time is fully utilized and the traffic congestion is effectively prevented, which makes the traffic flow run more efficiently and smoothly in the network. With rigorous analysis, the proposed control scheme guarantees the asymptotic convergence along the iteration axis. The simulation results show the effectiveness of the proposed method.