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The Bayesian combination method (BCM) proposed by Petridis et al. (2001) is an integrated method that can effectively improve the predictions of single predictors. However, research has found that it considers redundant prediction errors of component predictors when calculating their credits, which makes it quite impervious to the fluctuated accuracy of the component predictors. To address this problem, a new BCM has been developed here to improve the performance of the traditional BCM. It assumes that at one prediction interval, the traffic flow is correlated with the traffic flows of only a few previous intervals. With this assumption, the credits of the component predictors in the BCM are only accounted for by their prediction performance for a few intervals rather than for all intervals. Therefore, compared with the traditional BCM, the new BCM is more sensitive to the perturbed performance of the component …