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Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition
Author(s) -
Xie Yuanchang,
Zhang Yunlong,
Ye Zhirui
Publication year - 2007
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2007.00489.x
Subject(s) - kalman filter , wavelet , ensemble kalman filter , discrete wavelet transform , computer science , fast kalman filter , mathematics , algorithm , term (time) , extended kalman filter , wavelet transform , artificial intelligence , physics , quantum mechanics
This article investigates the application of Kalman filter with discrete wavelet analysis in short‐term traffic volume forecasting. Short‐term traffic volume data are often corrupted by local noises, which may significantly affect the prediction accuracy of short‐term traffic volumes. Discrete wavelet decomposition analysis is used to divide the original data into several approximate and detailed data such that the Kalman filter model can then be applied to the denoised data and the prediction accuracy can be improved. Two types of wavelet Kalman filter models based on Daubechies 4 and Haar mother wavelets are investigated. Traffic volume data collected from four different locations are used for comparison in this study. The test results show that both proposed wavelet Kalman filter models outperform the direct Kalman filter model in terms of mean absolute percentage error and root mean square error.