Short-term traffic prediction is of great importance to real-time traveler information and route guidance systems. Various methodologies have been developed for dynamic traffic prediction. However, many existing parametric studies focus on fixed-size data and presume time-invariant models. A proposed online adaptive model takes into account historical off-line data. A recursive algorithm is used to obtain computational efficiency and reduced storage. The algorithm is extended to a more general and flexible state-space model, and the predictions are computed recursively with a Kalman filter. A maximum likelihood off-line estimate of the noise covariance matrix and transition coefficients matrix is provided, as well as a recursive calculation of the optimal time-variant parameters on line. The result proves that the state-space model with the nonzero noise covariance matrix outperforms the other algorithms with loop detector data on Interstate 405 near Irvine, California.
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