Traffic Forecasting Using Least Squares Support Vector Machines

Accurate and timely forecasting of traffic parameters is crucial for effective management of intelligent transportation systems. Travel time index (TTI) is a fundamental transportation measure. In this article, a non-parametric technique called least squares support vector machines (LS-SVMs) is proposed to forecast TTI. To the auhtors' best knowledge, it is the first time to cooperate the rising computational intelligence technique with state space approach in traffic forecasting. Five other baseline predictors are selected for comparison purposes because of their proven effectiveness. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Experimental results demonstrate that the new predictor can significantly reduce mean absolute percentage errors and variance of absolute percentage errors, especially for predicting traffic data with weak regularity. Persuasive comparisons clearly show that it provides a large improvement in stability and robustness, which reveals that it is a promising approach in traffic forecasting and time series analysis.

Language

  • English

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  • Accession Number: 01152139
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Mar 16 2010 6:12AM