关键词: 交通工程, 预测模型, Volterra级数, 短时交通流, 乘积耦合

Abstract: An adaptive third-order Volterra filter for short-time traffic flow prediction was proposed, which was based on intrinsic nonlinearity and deterministic mechanism of chaotic time series and nonlinear expression of Volterra series. Concerning the problem that the coefficients of Volterra filter exponentially increase with its order, an approximate multiplication-coupled structure for Volterra filter was studied. At first, through properly choosing time delay and embedding dimension using mutual information method and false nearest neighbor method, respectively, the largest Lyapunov exponent was estimated by applying small data sets method so as to validate that chaos existed in traffic flow series. Then, the approximate multiplication-coupled structure for third-order Volterra filter, of which coefficients were adaptively adjusted by using an improved nonlinear Normalized Least Mean Square (NLMS) algorithm, was employed to reduce computational complexity. Finally, by applying the proposed method to the real measured traffic flow data, the experimental results show that chaos exists in traffic flow series and the proposed scheme can effectively predict traffic flow series and reduce model complexity.

Key words: traffic engineering, prediction model, Volterra series, short-time traffic flow, multiplication-coupled