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An adaptive method for predicting short-time traffic flow, which is based on phase space reconstruction and Volterra series was proposed. We first employed Lyapunav exponent method based small data sets to validate that chaos exists in traffic flow. Then, phase space reconstruction for traffic flow data was performed. And we constructed Volterra adaptive prediction model, which coefficients were updated by LMS adaptive algorithm. We finally applied this model to execute simulations for chaotic time series and real measured traffic flow data. Experimental results show that Volterra adaptive model can effectively predict chaotic time series and traffic flow.

Predicting Short-time Traffic Flow Using Volterra Adaptive Model