Paper Title:
Parallel SMO for Traffic Flow Forecasting
  Abstract

Accurate traffic flow forecasting is crucial to the development of intelligent transportation systems and advanced traveler information systems. Since Support Vector Machine (SVM)have better generalization performance and can guarantee global minima for given training data, it is believed that SVR is an effective method in traffic flow forecasting. But with the sharp increment of traffic data, traditional serial SVM can not meet the real-time requirements of traffic flow forecasting. Parallel processing has been proved to be a good method to reduce training time. In this paper, we adopt a parallel sequential minimal optimization (Parallel SMO) method to train SVM in multiple processors. Our experimental and analytical results demonstrate this model can reduce training time, enhance speed-up ratio and efficiency and better satisfy the real-time demands of traffic flow forecasting.

  Info
Periodical
Edited by
Qi Luo
Pages
843-848
DOI
10.4028/www.scientific.net/AMM.20-23.843
Citation
F. Wang, G. Z. Tan, C. Deng, "Parallel SMO for Traffic Flow Forecasting", Applied Mechanics and Materials, Vols. 20-23, pp. 843-848, 2010
Online since
January 2010
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Price
$32.00
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