Abstract
In this paper, a Genetic-Algorithm-based Artificial Neural Network (GAANN) model for short-term traffic flow forecasting is proposed. GAANN can integrate capabilities of approximation of Artificial Neural Networks (ANN) and of global optimization of Genetic Algorithms (GA) so that the hybrid model can enhance capability of generalization and prediction accuracy, theoretically. With this model, both the number of hidden nodes and connection weights matrix in ANN are optimized using genetic operation. The real data sets are applied to the introduced method and the results are discussed and compared with the traditional Back Propagation (BP) neural network, showing the feasibility and validity of the proposed approach.
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Liu, M., Wang, R., Wu, J., Kemp, R. (2005). A Genetic-Algorithm-Based Neural Network Approach for Short-Term Traffic Flow Forecasting. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_152
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DOI: https://doi.org/10.1007/11427469_152
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
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