摘要
通过对柳州市某对称交叉路口交通量状况的调查分析,提出一种基于自回归求和滑动平均(ARIMA)与径向基函数神经网络(RBF)非线性组合模型的短时交通量预测模型,利用实测数据对组合模型和单一模型进行仿真实验.实例分析表明:组合模型的预测结果比单一模型更加精确,适合于实时的短时交通量预测.
In the investigation and analysis of traffic flow situation at a symmetric intersection in the city of Liuzhou, a combined model which combined ARIMA （Autoregressive Integrated Moving Average） model and RBF neural network is proposed and set into a short-term traffic flow foreeasting simulation experiment. The result shows that the combined model prediction is more accurate than that of single model, and suitable for real-time short-term traffic flow orediction.
出处
《广西工学院学报》
CAS
2013年第2期6-9,共4页
Journal of Guangxi University of Technology
基金
广西自然科学基金项目(2011GXNSFF018004)资助
