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• W2010644681 abstract "Abstract Studying dynamic behaviours of a transportation system requires the use of the system mathematical models as well as prediction of traffic flow in the system. Therefore, traffic flow prediction plays an important role in today's intelligent transportation systems. This article introduces a new approach to short‐term daily traffic flow prediction based on artificial neural networks. Among the family of neural networks, multi‐layer perceptron (MLP), radial basis function (RBF) neural network and wavenets have been selected as the three best candidates for performing traffic flow prediction. Moreover, back‐propagation (BP) has been adapted as the most efficient learning scheme in all the cases. It is shown that the coefficients produced by temporal signals improve the performance of the BP learning (BPL) algorithm. Temporal signals provide researchers with a new model of temporal difference BP learning algorithm (TDBPL). The capability and performance of TDBPL algorithm are examined by means of simulation in order to prove that the wavelet theory, with its multi‐resolution ability in comparison to RBF neural networks, is a suitable algorithm in traffic flow forecasting. It is also concluded that despite MLP applications, RBF neural networks do not provide negative forecasts. In addition, the local minimum problems are inevitable in MLP algorithms, while RBF neural networks and wavenet networks do not encounter them." @default.
• W2010644681 created "2016-06-24" @default.
• W2010644681 creator A5022201943 @default.
• W2010644681 creator A5073252452 @default.
• W2010644681 date "2013-10-23" @default.
• W2010644681 modified "2023-10-18" @default.
• W2010644681 title "Application of temporal difference learning rules in short‐term traffic flow prediction" @default.
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• W2010644681 doi "https://doi.org/10.1111/exsy.12055" @default.
• W2010644681 hasPublicationYear "2013" @default.
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