Paper Title:
KF-MA Model for Short-Term Traffic Flow Prediction Based on SCATS Data
  Abstract

Aimed at the typical problems of lower accuracy and efficiency by using traditional Kalman Filtering (KF) to model the SCATS data, a KF model based on Moving Average (KF-MA) is put forward, that is, to find out the Markov properties of the SCATS data by Moving Average model at first then model the data by Kalman Filtering. Taking the SCATS data in Hang-Zhou as an example, the KF-MA model is compared with the Time Serial (TS) model, the Time Serial model based on Moving Average (TS-MA) and the KF model. The result shows that KF-MA model can maximum elevate the computational efficiency by reducing parameters need to solved in the traditional KF models. Moreover, compared with other models, KF-MA model has better predicted accuracy.

  Info
Periodical
Edited by
Ran Chen
Pages
3418-3422
DOI
10.4028/www.scientific.net/AMM.44-47.3418
Citation
W. Wu, H. D. Liu, W. G. Zhou, "KF-MA Model for Short-Term Traffic Flow Prediction Based on SCATS Data", Applied Mechanics and Materials, Vols. 44-47, pp. 3418-3422, 2011
Online since
December 2010
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$32.00
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