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Wavelet Packet‐Autocorrelation Function Method for Traffic Flow Pattern Analysis
Author(s) -
Jiang Xiaomo,
Adeli Hojjat
Publication year - 2004
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.1467-8667.2004.00360.x
Subject(s) - wavelet , wavelet packet decomposition , discrete wavelet transform , autocorrelation , computer science , traffic flow (computer networking) , second generation wavelet transform , algorithm , wavelet transform , mathematics , data mining , artificial intelligence , statistics , computer security
Accurate and timely forecasting of traffic flow is of paramount importance for effective management of traffic congestion in intelligent transportation systems. A detailed understanding of the properties of traffic flow is essential for building a reliable forecasting model. The discrete wavelet packet transform (DWPT) provides more coefficients than the conventional discrete wavelet transform (DWT), representing additional subtle details of a signal. In wavelet multiresolution analysis, an important decision is the selection of the decomposition level. In this research, the statistical autocorrelation function (ACF) is proposed for the selection of the decomposition level in wavelet multiresolution analysis of traffic flow time series. A hybrid wavelet packet‐ACF method is proposed for analysis of traffic flow time series and determining its self‐similar, singular, and fractal properties. A DWPT‐based approach combined with a wavelet coefficients penalization scheme and soft thresholding is presented for denoising the traffic flow. The proposed methodology provides a powerful tool in removing the noise and identifying singularities in the traffic flow. The methods created in this research are of value in developing accurate traffic‐forecasting models .