School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, China

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Abstract—The accurate traffic flow time series prediction is the prerequisite for achieving traffic flow inducible system. Aiming at the issue about multi-step prediction traffic flow chaotic time series, the traffic flow Volterra Neural Network (VNN) rapid learning algorithm is proposed. Combing with the chaos theory and the Volterra functional analysis, method of the truncation order and the truncation items is given and the VNN model of traffic flow time series is built. Then the mechanism of the chaotic learning algorithm is described, and the adaptive learning algorithm of VNN for traffic flow time series is designed. Last, a multi-step prediction of traffic flow chaotic time series is researched by traffic flow VNN network model, Volterra prediction filter and the BP neural network based on chaotic algorithm. The simulations show that the VNNTF network model predictive performance is better than the Volterra prediction filter and the BP neural network by the simulation results and rootmean- square value.

Index Terms—Chaos Theory, Phase Space Reconstruction, Time Series Prediction, VNN Neural Networks, Algorithm

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Cite: Lisheng Yin, Yigang He, Xueping Dong, and Zhaoquan Lu, " Multi-Step Prediction Algorithm of Traffic Flow Chaotic Time Series based on Volterra Neural Network," Journal of Computers vol. 8, no. 6, pp. 1480-1487, 2013.