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Traffic flow forecasting is an important application of computational intelligence and an active research topic in Intelligent Transportation Systems (ITS). However, traditional methods called single-link traffic flow forecasting usually predict one link's unidirectional traffic flow at a time, which do not take the relevance of adjacent links into account and make the ITS have a low efficiency. In...
Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks. In contrast to STL, multitask learning (MTL) has the potential to improve generalization by transferring information in training signals of extra tasks. In this paper, MTL based neural networks are used for traffic...
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