基于遗传神经网络优化模型的交通量预测  

Forecast of Traffic Volume Based on Genetic Neural Network Optimization Model

作　　者：许伦辉[1] 刘正东[1] 周文霞[1] 习利安[1] 

机构地区：[1]华南理工大学交通学院,广东广州510641

出　　处：《广东工业大学学报》2006年第4期1-5,共5页Journal of Guangdong University of Technology

基　　金：国家自然科学基金(60064001);广东省自然科学基金(20011707)

摘　　要：实时、准确的交通量预测是实现动态交通流控制及诱导的前提和基础.为了更准确地对其进行预测,本文建立了遗传神经网络优化模型,该模型既利用遗传算法全局搜索、快速收敛的优点,又利用神经网络非线性描述、自学习自适应的优点.并以实际道路为例,给出了具体的应用方法,计算机仿真结果表明该模型精度较高、具有可行性.The accurate real-time forecast of traffic volume is the premise and basement of the dynamic traffic control and guidance. In order to forecast the tratffic volume more accurately, this paper establishes an optimization model of genetic neural network. It not only utilizes the advantages of the genetic algorithm which are global search and rapid convergence, but also makes use of the merits of the neural network which are nonlinearly describing, self learning and self adapting. It has been applied on a real road to forecast the traffic volume and the computer simulations have proved that the optimization model has great precision and feasibility.

关 键 词：神经网络 遗传算法 优化 交通量预测 

分 类 号：U121[交通运输工程]