摘要
针对智能交通系统的开发,提出一种基于灰色GM(1,1)模型和RBF网络非线性组合的短时交通流预测方法.该方法采用三层结构的RBF网络将两种单一预测方法(灰色GM(1,1)模型和RBF网络)进行了非线性组合.利用实测数据对组合方法进行了仿真实验,结果表明:非线性组合模型的预测准确性高于单独的RBF网络预测的准确性;组合模型发挥了两种单一方法各自的优势,是短时交通流预测的有效方法.
Aimed at developing Intelligent Transportation System, combined RBF network and GM（1,1） forecast, a new method of short-term traffic flow prediction is put forward. The hybrid forecasting approach combined the two methods making use of the non-linear RBF neural network which has a structure of three layers. The simulation test of the forecasting approaches was taken placed used field data. Results show that the non-linear hybrid model, which takes advantage of the unique strength of the two models in linear and nonlinear modeling, can produce more accurate predictions than that of single model. The hybrid model can be an efficient method to the short-term traffic flow prediction.
出处
《数学的实践与认识》
CSCD
北大核心
2011年第19期1-7,共7页
Mathematics in Practice and Theory
基金
国家自然科学基金(61074140)
山东省自然科学基金(ZR2010FM007)
山东理工大学青年教师发展支持计划
