基于递阶遗传算法优化的交通流预测

Traffic Flow Prediction Based on Hierarchical Genetic Optimized Algorithm

及时准确地进行短时交通流预测是智能交通系统研究的关键内容之一。基于小波分析和模糊神经网络的相关知识,提出模糊小波神经网络的控制方法。将小波函数作为模糊隶属函数,利用神经网络实现模糊推理,从而完成对下一周期交通流量的预测,同时采用递阶遗传算法实现网络结构和参数的优化。经实测数据验证,预测精度高,运行稳定,适应性强。

The forecast of real-time traffic flow is one of important contents of intelligent transportation system research.Based on the related knowledge of wavelet analysis and fuzzy neural networks,this paper proposes the fuzzy wavelet neural networks control method.It takes wavelet function as fuzzy membership function,uses neural networks to realize fuzzy reasoning,and finishes the estimation of next cyclical traffic flow.Simultaneously the genetic algorithm is used to optimize the overall network.After the field data test,this method is high precise,stable and compatible.