城市交通信号的迭代学习控制及其对路网宏观基本图的影响  

Iterative learning control for urban traffic signals and the impacts on macroscopic fundamental diagram of road networks

作　　者：闫飞[1] 田福礼[1] 史忠科[1] 

机构地区：[1]西北工业大学自动化学院,陕西西安710129

出　　处：《控制理论与应用》2016年第5期645-652,共8页Control Theory & Applications

基　　金：国家自然科学基金重点项目(61134004)资助~~

摘　　要：针对在城市交通信号控制中存在对交通流难以精确建模的问题,首先利用交通流的重复性特点,提出了一种基于迭代学习的城市交通信号控制方法,并证明了在不确定初态下迭代学习控制算法的收敛性.其次,结合路网宏观基本图的特性分析了基于迭代学习的交通信号控制策略对路网交通态势的影响.结果表明,当迭代的初始状态在期望初态值的小范围内波动时,系统的跟踪误差仍能收敛到一个界内;通过对交通信号的迭代学习控制,路段的实际占有率能够逐步逼近期望占有率,从而使路网内的车辆密度分布更加均匀,确保交通流在更优的宏观基本图下运行,防止因车辆密度分布不均引起的通行效率下降及交通拥堵的发生.最后,通过仿真实验对所提方法的有效性进行了验证.Aiming at the difficult problem of accurately modeling traffic flow in urban traffic signal control, an iterative learning control（ILC） approach for urban traffic signals is first presented by using the repeatability characteristics of traffic flow, and the convergence of the ILC algorithm with initial state uncertainty is proved by rigorous analysis. Then, the impacts of the iterative learning based traffic signal control strategy on the traffic conditions of road networks are analyzed by using the property of macroscopic fundamental diagram（MFD）. The analysis results show that uniform bounds for the system tracking errors are obtained when the initial states of traffic flow fluctuate in small ranges. The actual space occupancies of each link in the network can gradually approximate the desired ones through iterative control of the traffic signals, which makes the vehicle density distribution in the network be more homogenous and ensures traffic flows run under a well defined MFD. Therefore, the traffic efficiency decline and traffic congestion caused by heterogeneous distribution of vehicle density are effectively prevented. Finally, the effectiveness of the proposed method is verified by simulation tests.

关 键 词：迭代学习控制 占有率 交通信号控制 收敛性分析 宏观基本图 

分 类 号：U491.54[交通运输工程—交通运输规划与管理]