KF-MA Model for Short-Term Traffic Flow Prediction Based on SCATS Data

Abstract:

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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 et al., "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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Price:

$35.00

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