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

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