Bi‐level Programming Formulation and Heuristic Solution Approach for Dynamic Traffic Signal Optimization
Bi‐level Programming Formulation and Heuristic Solution Approach for Dynamic Traffic Signal Optimization
复制标题
DOI：
10.1111/j.1467-8667.2006.00439.x
复制
发表时间：
2006-07
期刊：
影响因子：
--
通讯作者：
Dazhi Sun;R. Benekohal;S. Travis Waller
中科院分区：
文献类型：
--
作者：
Dazhi Sun;R. Benekohal;S. Travis Waller
摘要：在智能交通系统（ITS）框架下，动态交通控制和交通分配是紧密联系在一起的，但现有的研究大多是将两者相互独立地发展起来。传统的信号配时优化方法是在给定交通流模式的前提下进行的，而交通分配是在信号配时固定的前提下进行的。本文提出了一种双级规划公式和启发式求解方法（HSA），用于具有时间依赖性需求和随机路径选择的网络中的动态交通信号优化。在双层规划模型中，上层问题表示系统管理者的决策行为（信号控制），而用户的出行行为表示在下层。HSA由遗传算法（GA）和基于小区传输模拟（CTS）的增量Logit分配（ILA）过程组成。采用遗传算法求上电平信号控制变量。在底层开发了ILA来寻找用户最优的流模式，并实现了CTS来传播交通和收集实时交通信息。在一个样本网络中，对HSA的性能进行了数值研究。这些应用比较了精英遗传算法和微遗传算法的效率和质量。此外，还分析了不同信息更新频率和不同遗传算法种群大小对系统性能的影响。
Abstract: Although dynamic traffic control and traffic assignment are intimately connected in the framework of Intelligent Transportation Systems (ITS), they have been developed independent of one another by most existing research. Conventional methods of signal timing optimization assume given traffic flow pattern, whereas traffic assignment is performed with the assumption of fixed signal timing. This study develops a bi‐level programming formulation and heuristic solution approach (HSA) for dynamic traffic signal optimization in networks with time‐dependent demand and stochastic route choice. In the bi‐level programming model, the upper level problem represents the decision‐making behavior (signal control) of the system manager, while the user travel behavior is represented at the lower level. The HSA consists of a Genetic Algorithm (GA) and a Cell Transmission Simulation (CTS) based Incremental Logit Assignment (ILA) procedure. GA is used to seek the upper level signal control variables. ILA is developed to find user optimal flow pattern at the lower level, and CTS is implemented to propagate traffic and collect real‐time traffic information. The performance of the HSA is investigated in numerical applications in a sample network. These applications compare the efficiency and quality of the global optima achieved by Elitist GA and Micro GA. Furthermore, the impact of different frequencies of updating information and different population sizes of GA on system performance is analyzed.