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This paper provides a neural network model to address the problem of travel time prediction. A single segment model based on the state space neural network is used for modeling traffic flow on one single signalized segment. Thus, modelling a longer arterial covering several controlled intersections is conducted by assembling each individual segment models. This reduces significantly the amount of parameters of the neural network, which make it simpler and easier to be implemented in practice. An urban arterial in the Netherlands was selected as test bed. The results indicate that this proposed model is capable of dealing with complex nonlinear urban arterial travel time predictions with satisfying accuracy.