论文标题
分布式随机模型的预测控制
Distributed Stochastic Model Predictive Control for an Urban Traffic Network
论文作者
论文摘要
在本文中，当考虑到外源性（in/out）流的不确定性时，我们为城市交通网络设计了随机模型预测控制（MPC）交通信号控制方法，并考虑到下游交通流量的转向比。假设流量模型参数是具有已知期望和差异的随机变量，则交通信号控制和协调问题被提出为具有线性和二阶锥体约束的二次程序。为了降低计算复杂性，我们建议一种将与整个流量网络相对应的优化问题分解为多个子问题的方法。通过应用乘数的交替方向方法（ADMM），以分布式方式找到了最佳随机交通信号拆分。设计控制方法的有效性通过使用Vissim和Matlab的一些模拟验证。
In this paper, we design a stochastic Model Predictive Control (MPC) traffic signal control method for an urban traffic network when the uncertainties in the estimation of the exogenous (in/out)-flows and the turning ratios of downstream traffic flows are taken into account. Assuming that the traffic model parameters are random variables with known expectations and variance, the traffic signal control and coordination problem is formulated as a quadratic program with linear and second-order cone constraints. In order to reduce computational complexity, we suggest a way to decompose the optimization problem corresponding to the whole traffic network into multiple subproblems. By applying Alternating Direction Method of Multipliers (ADMM), the optimal stochastic traffic signal splits are found in distributed manner. The effectiveness of the designed control method is validated via some simulations using VISSIM and MATLAB.
