Short-term Traffic Flow Forecasting via Echo State Neural Networks

摘要：

An algorithm for short term traffic flaw prediction based on echo state neural networks (ESN) is proposed in this paper. ESN is a new paradigm for using recurrent neural networks (RNNs) with a simpler training method. While the prediction, traffic flow patterns are treated as time series signals; no further information is used than the past traffic flaw data records, such as weather, traffic accidents. The relation between key parameter of the ESN and the predicting performance is discussed; ESN and feed forward neural network (FNN) are compared with the same task also. Simulation experiment results demonstrate that the proposed ESN algorithm is valid and can obtain more accurate predicting results than the FNN for the short-term traffic flaw prediction problem.

关键词： echo state neural networks traffic flow prediction

作者: An Yisheng Song Qingsong Zhao Xiangmo

作者单位: School of Information Engineering Changan University Xian, Shaanxi, China

会议类型: 国际会议

会议名称: 2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

会议地点: 上海

会议语种:英文

页码: 861-864

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）