Abstract
Abstract: This article addresses the problem of the accuracy of short‐term traffic flow forecasting in the complex case of urban signalized arterial networks. A new, artificial intelligence (AI)‐based approach is suggested for improving the accuracy of traffic predictions through suitably combining the forecasts derived from a set of individual predictors. This approach employs a fuzzy rule‐based system (FRBS), which is augmented with an appropriate metaheuristic (direct search) technique to automate the tuning of the system parameters within an online adaptive rolling horizon framework. The proposed hybrid FRBS is used to nonlinearly combine traffic flow forecasts resulting from an online adaptive Kalman filter (KF) and an artificial neural network (ANN) model. The empirical results obtained from the model implementation into a real‐world urban signalized arterial demonstrate the ability of the proposed approach to considerably overperform the given individual traffic predictors.
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