基于Echo State Neural Networks的短期交通流预测算法

在城市交通环境,交通流的正确预测是比较困难,因为多个十字路口,这使得预置的交通控制模型之间的相互作用和intertwinement不能保持始终高性能在所有的交通情况。

Algorithm for Short-term Traffic Flow Prediction Based on Echo State Neural Networks

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.