Abstract: Artificial neural network forecasting model is an efficient method to forecast the short-term traffic flow, but it is hard to choose the proper input variables and the number of hidden neurons. A data-driven algorithm was proposed to estimate the network structure of neural network forecasting model. According to the chaotic characteristics of the short-term traffic flow, phase space reconstruction was introduced to choose input variables reasonably. Then the number of hidden neurons can be estimated by the fast monotonic value estimation method. The experiment at result demonstrates the efficiency of the proposed algorithm on estimating the network structure of forecasting model.

Key words: short-term traffic flow forecasting, neural network model, network architecture, hidden neuron, phase-space reconstruction

摘要： 针对神经网络预测模型在预测短时交通流时输入变量选取与隐含神经元数目确立上的不足,提出了一种数据驱动的快速网络结构估计算法。根据交通流的混沌特性,引入相空间重构的思想合理地选择模型的输入变量;再结合快速单调指数估计法迅速计算重构向量的单调指数,并将其值作为隐层神经元个数,继而确立整个预测模型的网络结构。实验结果表明,该算法能有效地估计模型的网络结构以满足短时交通流预测的需要。

关键词: 短时交通流预测, 神经网络模型, 网络结构, 隐层神经元, 相空间重构