Optimized and Meta-Optimized Neural Networks for Short-Term Traffic Flow Prediction: A Genetic Approach

Neural networks are one of the best alternatives for modeling and predicting traffic parameters such as flow and occupancy. Because of limited knowledge regarding a network’s optimal structure given a specific dataset, researchers have to rely on time-consuming and questionably efficient rules-of-thumb when developing these neural networks. This paper extends past research by providing an advanced, genetic algorithm-based, multilayered structural optimization strategy that can assist both in the proper representation of traffic flow data with temporal and spatial characteristics as well as in the selection of the appropriate neural network structure. It also evaluates the performance of the developed network by applying it to both univariate and multivariate traffic flow data from an urban signalized arterial. The results show that the capabilities of a simple static neural network, with genetically optimized step size, momentum and number of hidden units, are very satisfactory when modeling both univariate and multivariate traffic data.

Language

  • English

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Filing Info

  • Accession Number: 01004239
  • Record Type: Publication
  • Files: TRIS, ATRI
  • Created Date: Sep 27 2005 10:44PM